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IR Automation Guidebook:

Temperature Monitoring and Control with IR Cameras

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IR Automation Guidebook:

Temperature Monitoring and Control with IR Cameras

The thoughts, ideas, opinions, and recommendations expressed in this book are intended for informational purposes only. FLIR accepts no liability for actions taken by readers in their individual businesses or circumstances.

Published by FLIR Systems Incorporated This booklet may not be reproduced in any form without the permission in writing from FLIR Systems Incorporated.

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iii

Contents

Preface iv

Chapter 1

Typical Monitoring and Control Applications 1

Chapter 2

Remote IR Monitoring 5

Chapter 3

Temperature Measurement for Automated Processes 17

Chapter 4

Combining Machine Vision and Temperature Measurement 25

Chapter 5

Real-Time Control Issues 32

Appendix A

Glossary 40

Appendix B

Thermographic Measurement Techniques 43

Appendix C

History and Theory of Infrared Technology 45

Appendix D

Command Syntax Examples for A320 Resource Socket Services 58

Appendix E

Quick Summary of FLIR IR Cameras

Inside Back Cover

Preface

Manufacturing and process engineers are under constant pressure to make production systems and processes more

ecient and less costly. Frequently, their

solutions use automation techniques to

improve throughput and product quality. Automated IR (infrared) radiation imaging oers the potential for improving a host of industrial production applications,

including process monitoring and

control, quality assurance, asset management, and machine condition

monitoring.

This handbook is intended to help those

considering the creation or improvement

of production automation or monitoring

systems with IR cameras. Numerous

application examples will be presented

with explanations of how these IR vision

systems can best be implemented.

Some of the major topics that will be

covered include:

Integration of IR cameras into • automation systems

Data communications interfaces• Command and control of •

thermographic cameras Principles of thermographic •

measurements Interfacing with a PC or PLC controller• Standard software packages for IR •

camera systems

These complex matters require attention

to many details; therefore, this handbook cannot answer every question a system designer will have about the use of

IR cameras in automated systems. It

is meant to serve only as a roadmap

through the major issues that must be

faced in IR vision system design.

Typical Monitoring and

Control Applications

Typical Monitoring and

Control Applications

Temperature Measurements with IR Cameras

Infrared (IR) radiation is not detectable by the human eye, but an IR camera can convert it into a visual image that depicts thermal variations across an object or scene. IR covers a portion of

the electromagnetic spectrum from

approximately 900 to 14,000 nanometers (0.9–14 µm). IR is emitted by all objects at temperatures above absolute zero, and

the amount of radiation increases with

temperature. A properly calibrated IR

camera can capture thermographic images

of target objects and can provide accurate non-contact temperature measurements of those objects. These quantitative measurements can be used in a variety of

monitoring and control applications.

In contrast, other types of IR imagers provide only relative temperature

dierences across an object or scene.

Hence, they are used to make qualitative assessments of the target objects,

primarily in monitoring applications where thermal images are interpreted

based on temperature contrast. One

example is to identify image areas that

correlate to physical anomalies, such as construction or sub-surface details, liquid levels, etc.

In some cases, an IR camera is justiably

referred to as a smart sensor. In these

cases the IR camera has built-in logic

and analytics that allows the comparison

of measured temperatures with user-

supplied temperature data. It also has a

digital I/O interface so that a dierential

temperature can be used for alarm and

control functions. In addition, a smart

IR camera is a calibrated thermographic

instrument capable of accurate non-

contact temperature measurements.

IR cameras with these capabilities operate much like other types of smart

temperature sensors. They have fast, high-resolution A/D (Analog to Digital) converters that sample incoming data, pass it through a calibration function, and provide temperature readouts. They may also have other communication interfaces that provide an output stream of analog

or digital data. This allows thermographic images and temperature data to be transmitted to remote locations for process monitoring and control.

Generally, smart IR cameras are used in quantitative applications that

require accurate measurements of the temperature dierence between a target object and its surroundings. Since temperature changes in most processes

are relatively slow, the near-real-time data

communications of smart IR cameras are adequate for many process control loops

and machine vision systems.

Automation Applications

Typical automated applications using IR cameras for process temperature monitoring and control include:

Continuous casting, extrusion, and roll •

forming Discrete parts manufacturing• Production where contact temperature •

measurements pose problems Inspection and quality control• Packaging production and operations•

Chapter 1

Environmental, machine, and safety •

monitoring Temperature monitoring as a proxy for •

other variables

The examples below demonstrate a wide

range of applications that can be served

with IR cameras. Potential applications are limited only by the imagination of the system designer.

Plywood Mill Machine Monitoring

Problem: Steam from open vats of hot water obscures the machinery operator’s view of the logs as they are maneuvered for proper alignment in the log vat.

Solution: An IR camera can present an

image to the operator that makes the

cloud of steam virtually transparent,

thereby allowing logs to be properly

aligned in the log vat. This example of a qualitative application is illustrated in Figure 1.

Production Testing of Car Seat Heaters

Problem: Using contact temperature

sensors to assure proper operation of optional car seat heaters slows down production and is inaccurate if sensors are not properly placed.

Solution: An IR camera can detect

thermal radiation from the heater

elements inside the seats and provide an accurate non-contact temperature

measurement.

This quantitative measurement can be

made with a camera that is permanently

mounted on a xture that is swung

into measurement position when the car reaches a designated point

on the assembly line. A monitor near that position provides an image with a temperature scale that reveals the

temperature of the car seat heater

elements, as shown in Figure 2.

The Problem

• Operatorscannotseethroughthesteam cloudcausedbycondensationincoolerair temperatures.

The Solution

• IRoersanotherpairof“eyes”tosee throughthesteamintothelogvatfor properlogalignment.

Figure 1. Plywood mill application

Typical Monitoring and Control Applications

Packaging Operations

Problem: On a high-speed packaging line, ecient methods for nondestructive testing of a glued box seal are scarce, and most tend to be very cumbersome. In addition, the glue

application method has a good deal of

variability that must be monitored and

recorded with statistical quality control routines.

Solution: Since the glue is heated prior

to application, its temperature and

The Problem

• Optionalfeaturesinvehiclescannotbe inspectedwithoutsometypeofcontact.

• Thisslowsdownproduction.

• 100%inspectionistedious.

The Solution

• AnIRcameracanbepermanentlymountedto inspecttheseitems.

• AnIRcameracanbeusedinanon-contact method.

Figure 2. Production testing of car seat heater elements

The Problem

• Detectincorrectlysealedboxes.

• Removefailedunitsfromtheline.

• Generateanalarmiftoomanyboxesfail.

• Logstatisticaldataofpass/fail.

The Solution

• Captureathermalimageofthebox.

• Detectpresenceofgluespots.

• Pass/failoneachbox.

• Logstatistics.

Figure 3. Machine vision box seal quality control

Chapter 1

locations on the box lid can be monitored

with an IR camera. Moreover, the image can be digitized in a way that allows this

information to be stored in a statistical quality control database for trend analysis and equipment monitoring as shown in

Figure 3.

This is an example of using dierential temperature as a proxy for another

variable. In this case, temperature

replaces mechanical methods of inspection/testing.

Summary

The automation examples presented

in this chapter have barely scratched

the surface of the application space

that smart IR cameras can serve. In the following chapters, more detailed

examples will be presented along with practical information on the implementation of automated systems

that exploit the advantages of IR cameras. These chapters are organized according

to the major types of applications that typically use IR cameras:

Remote thermographic monitoring•

Non-contact temperature •

measurement for automated processes

Combining IR machine vision with •

temperature measurement

Real-time control and monitoring – •

issues and answers

Remote IR MonitoringChapter 

Remote IR Monitoring

Overview

Infrared radiation is emitted by all objects

at temperatures above absolute zero

and is detectable by IR cameras. Since

these cameras have various means of

communicating thermographic images

and temperatures to remote locations,

they are ideal for remote and unattended

monitoring. Moreover, smart IR cameras (those with built-in logic, analytics, and data communications), can compare

the temperatures obtained from their

thermographic images with user-dened

settings. This allows the camera to output a digital signal for alarm and

control purposes, while also providing live images.

IR Camera Operation

IR camera construction is similar to

a digital video camera. The main

components are a lens that focuses IR

onto a detector, plus electronics and

software for processing and displaying thermographic images and temperatures

on an LCD or CRT monitor (Figure 1). Instead of a charge coupled device that video and digital still cameras use,

the IR camera detector is a focal plane

array (FPA) of micrometer size pixels made of various materials sensitive to

IR wavelengths. FPA resolution ranges from about 80×80 pixels up to 1024×1024 pixels. In some IR cameras, the video

processing electronics include the logic and analytical functions mentioned

earlier. Camera rmware allows the user to focus on a specic area of the FPA or use the entire detector area for calculating minimum, maximum, and average temperatures. Typically,

temperature measurement precision is ±°C or better.

The camera lens and distance to the

target object results in a eld of view (FOV) that determines the spot size covered by each pixel. The pixel’s analog

output represents the intensity of heat

energy received from the spot it covers on the target object. In FLIR IR cameras, the A/D converters that digitize the pixel output have resolutions that range from 8 bits (28 or 0–255 pixels) up to 14 bits (214 or 0–16383 pixels). The thermographic

image seen on the monitor screen is the result of a microprocessor mapping

these pixel output values to a color or gray scale scheme representing relative temperatures. In addition, radiometric

information associated with the heat energy impinging on a pixel is stored for use in calculating the precise

temperature of the spot covered by

that pixel.

IR In

Optics

NIR

MWIR

LWIR

Video Processing Electronics

Detector Cooling

Digitization

User Interface

User Control

Video Output

Digital Output

Synchronization In/Out

System Status

Figure 1. Simplied block diagram of an IR camera

Chapter 

Hence, IR cameras with these capabilities

operate much like other types of smart temperature sensors. Their calibrated

outputs can be accessed via one or more

communication interfaces and monitored

at a remote location. Images saved from

these cameras are fully radiometric1 and

can be analyzed o-line with standard software packages, such as those available from FLIR.

Important Criteria in Remote Monitoring Systems

When considering an IR camera for a

remote monitoring system, some of the important variables to consider are:

Spot size – the smallest feature in a •

scene that can be measured

FOV (Field of View) – the area that the •

camera sees

Working distance – distance from the •

front of the camera lens to the nearest target object

Depth of eld – the maximum depth of •

a scene that stays in focus

Resolution – the number of pixels and • size of the sensor’s active area

NETD (Noise Equivalent Temperature • Dierence) – the lowest level of heat

energy that can be measured

Spectral sensitivity – portion of •

the IR spectrum that the camera is

sensitive to Temperature measurement range, •

precision, and repeatability – a function of overall camera design

1 Radiometry is a measure of how much energy is

radiating from an object, as opposed to thermography,

which is a measure of how hot an object is; the two are related but not the same.

Another fundamental consideration is which portion of a camera’s FOV

contains the critical information required for monitoring purposes. The

objects within the FOV must provide an

accurate indication of the situation being

monitored, based on the temperature

of those objects. Depending on the

situation, the target objects may need

to be in the same position consistently

within the camera’s FOV. Other application variables related to the

monitored scene include:

Emissivity of the target objects• Reected temperatures within the FOV• Atmospheric temperature and •

humidity

These topics will be covered in more

detail in a subsequent chapter.

Remote Asset Monitoring

One type of application where IR cameras are very useful is in remote monitoring of property, inventory, and other assets to help prevent loss and improve safety. Frequently, this involves storage facilities,

such as warehouses or open areas for bulk materials. The following example

can serve as a general model for setting

up an IR camera monitoring system for this type of application.

Hazardous Waste Storage Monitoring. In this application barrels of chemical waste

products are stored in a covered facility,

but one in which they cannot be totally

protected from moisture. Thus, there is

the possibility of leaks or barrel contents becoming contaminated by air and

moisture, causing a rise in temperature due to a chemical reaction. Ultimately, there is a risk of re, or even an explosion.

Remote IR Monitoring

While visible light cameras might be used in such an application, there often is a line-of-sight problem where many of the barrels cannot be seen, even with

multiple cameras positioned throughout

the storage area. In addition, smoke or ames would have to be present before a visible light camera could detect a

problem. This might be too late for

preventative measures to be taken. In contrast, stand-alone IR cameras

monitoring the facility can detect a

temperature rise within their FOV before re occurs (Figures 2a and 2b).

Depending on the camera manufacturer, several monitoring options are available. For instance, the FLIR A320 camera allows a threshold temperature value to

be set internally for alarm purposes. In

addition, the camera’s logic and clock functions can be congured so that a rise

in temperature must be maintained for a certain period of time before an alarm is sent. This allows the system to ignore a

temporary temperature rise in a camera’s FOV caused by a forklift entering the area

to add or remove barrels. Furthermore,

a hysteresis function can also be used to

prevent an alarm from turning o until

the detected temperature falls well below

the setpoint (Figure 3).

Cameras with a digital I/O interface typically provide an OFF/ON type of

output for alarm purposes. The digital

I/O output is either o or on; when on, it is typically a DC voltage or current. For example, the digital I/O output from a FLIR A320 camera is 10–30VDC for loads of 100mA or less. Typically, the digital I/O output is sent to a PLC (Programble Logic Controller) that controls the portion

of an alarm system associated with the monitored area.

A good way to set up the alarm system is to have all cameras congured so they have a high level digital output when the

temperature is below the alarm condition

that holds a PLC in its non-alarm state.

When the alarm setpoint temperature is

detected, the camera’s digital I/O output goes low (typically zero volts) after an appropriate time delay, causing the PLC

Figure 2a. IR image of a hazardous waste storage area showing two spot temperature readings (26.4°F and 16.8°F) that are in the safe range, plus one reading (98.8°F) that is abnormally high.

Figure 2b. A subsequent image of the same area shows that the abnormal reading in 2a has increased further, causing an alarm to go o.

Chapter 

to go into its alarm state. This creates a

fail-safe system. If power to the camera is lost, then there is no high level output to the PLC, which treats that event just as if a temperature had reached the setpoint,

thereby causing an alarm. This alerts

personnel that they have either lost the

monitoring function or there is indeed a temperature rise.

Image monitoring. Receiving a warning

based on temperature measurements is

very useful, but the real power of IRbased asset monitoring is in the camera’s

image processing capabilities. Control

room personnel can get live images from IR cameras that visible light cameras

and other temperature detectors

cannot provide. Again, cameras vary by manufacturer, but the most versatile ones oer a variety of data communication

formats for sending thermographic

images to remote locations. Increasingly, web-enabled cameras are used to allow

monitoring from any location where a PC

is available.

Figure 4 illustrates a system using the FLIR A320’s Ethernet and TCP/IP

communication protocols in conjunction with its alarm setpoint capabilities. The Ethernet portion of the system allows

cable runs of up to 100 meters in length.

By communicating a digital alarm directly

to the PLC, it can immediately activate a visual and/or audible alarm. The visual

alarm can appear on an annunciator panel telling the operator where the alarm originated; the operator then goes

to the PC to look at live image(s) of that

location. Images and temperature data can be stored for future reference and analysis.

A320 cameras can also be congured to

automatically send temperature data

and images to a PC via e-mail (SMTP) or FTP protocol whenever the temperature setpoint is reached, thereby creating a record for subsequent review.

Time

Temperature

Threshold T e mperature (Warning On)

Wa rning O T e mperature

Deadband

O Te mp = On Te mp – Deadband

Hysteresis

• Also known as deadband

• Can be thought of as another threshold setting – where the smart sensor resets the alarm that was generated when the original setpoint was compromised

• Used to prevent signal “chatter”

Figure 3. Hysteresis is an important signal processing characteristic of smart IR cameras, which makes monitoring and control functions much more eective.

Remote IR Monitoring

In conjunction with a host controller

running FLIR’s IR MONITOR (or other suitable software), temperature data can be captured for trend analysis. The A320

can also supply a digital compression

of the camera’s analog video signal, which can be sent as MPEG-4 streaming digital video over an Ethernet link to a PC monitor. IR MONITOR can be used to set up temperature measurements, image capture, and camera display functions.

This application allows the PC to display up to nine camera images at a time and switch between additional camera

groups as needed. The FLIR IP CONFIG

software can be used to set up each

camera’s IP address.

After the cameras are congured, the

PC used for monitoring does not need to remain on the network continually. By using the FTP and SMTP protocols

within the camera, the user can receive radiometric images upon alarm events or on a time based schedule. Also, any

available PC with a web browser can be used to access the cameras web server for live video and basic control. This web

interface is password protected.

Most IR cameras have an analog video output in a PAL or NTSC format. Therefore, another image monitoring

possibility is to use a TV monitor to

display thermographic video. A single

control room monitor can be used with

a switch to view live images from each

camera sequentially. When the cameras

are properly congured, control room personnel can view scaled temperature readings for any point or area (minimum, maximum, and average) in that image. (See color scales in the screen capture images depicted in Figure 2.) Not only

will the operator know when there

is excessive heat, he or she can see

where it is.

Another example of the innovative functions available in camera rmware

or external software is a feature called

Figure 4. An example of one type of system conguration for remote IR camera monitoring. The system uses a digital alarm output for annunciating an over-temperature condition and transmits streaming MPEG-4 compressed video that allows the scene to be viewed on a PC monitor.

Use Digital Out on each camera to ALARM on AREA MAX

Use the camera’s web interface to congure multiple cameras. Set up one AREA in each camera.

Chapter 

image masking. This enables the user

to pre-select specic areas of interest

for analysis of temperature data. This

is illustrated in Figure 5, which shows

continuous monitoring of substation hotspots that indicate problem areas.

A similar type of pattern recognition

software can be used for automated inspection in metal soldering and welding and in laser welding of plastic parts. IR cameras can see heat

conducting through the nished parts

to check the temperature of the areas where parts are joined together against

a stored value. In addition, the software

can learn a weld path to make sure this

path is correct, which is accomplished by programming the specic pixels in

an image to be used by the software for

this purpose. Alternatively, the program

developer can save an image of a “perfect” part and then have the software look for minimum, maximum, or delta values that tells the equipment operator

if a part passes inspection. The car seat

heater inspection described in Chapter 1 can be an example of this, and the same

principle is used in the inspection of car window heater elements by applying power to them and looking at their thermographic image.

Power over Ethernet. It should be noted

that a camera with Ethernet connectivity can be powered from a variety of sources, depending on its design. Typically, a

connection for an external DC supply is

used, or where available, the camera is powered via PoE (Power over Ethernet).

PoE uses a power supply connected to the network with spare signal leads not

Figure 5. Masking functionality of the FLIR A320 IR camera, which is also available in some third party software programs.

Remote IR Monitoring

otherwise used in 10/100baseT Ethernet systems. Various PoE congurations are possible. Figure 6 depicts one in which

the power source is located at one end

of the network. (Gigabit Ethernet uses all available data pairs, so PoE is not possible with these systems.)

PoE eliminates the need for a separate power source and conduit run for each camera on the network. The only additional cost is for some minor electrical hardware associated with PoE.

Many applications encompass areas that exceed the maximum Ethernet cable

run of 100m. In those cases, there are wireless and beroptic converter options that provide o-the-shelf solutions for communicating over much greater

distances. These are frequently used in the bulk material storage applications described below.

Additional Asset Monitoring Situations

Bulk Material Storage. Many bulk materials are stored in open yards where air and moisture can help promote decomposition and other exothermic reactions that raise the temperature of the pile. This brings with it the threat

of re, direct monetary loss, and safety issues for personnel. In addition, there

is the risk of consequential damages

caused by res, including loss of nearby property, water damage resulting from re-ghting, and production shutdowns.

Materials that are especially prone to spontaneous combustion include organic

wastes (compost, etc.), scrap paper for recycling, wood, coal, and various inorganic chemicals, such as cement and chlorine hydrates. Even in the absence of spontaneous combustion, many bulk materials like plastics pose a re hazard due to sparks or other external

ignition sources.

Spare Pair

Signal Pair

+48V

–

DC/DC

Converter

RX TX

Spare Pair

Power Sourcing

Equipment (PSE)

Powered

Device (PD)

Figure 6. Schematic depicting spare-pair PoE delivery using the endpoint PSE arrangement.

Chapter 

In most cases, prevention is less costly than a cure, and the best prevention is

continuous monitoring of the materials. The cost of an automated temperature monitoring system using IR cameras is

a modest and worthwhile investment.

System design can take the same form as

the one described earlier for hazardous waste barrels. Cameras are congured

to generate a direct alarm output to an

operator when user-dened maximum

temperature thresholds are exceeded.

Audible and visual alarms in a control room draw the operator’s attention to a possible spontaneous re development. Various types of software have been developed to isolate trouble spots, such as the waste pile zone monitoring system depicted in Figure 7.

Although self-ignition usually starts within the bottom layers of a stock pile,

continuous monitoring of the surface

reveals hot spots at an early stage (Figure

8), so measures can be taken to prevent a major re from breaking out. Large

storage yards generally require multiple

cameras for total coverage, with the cameras mounted on metal masts above

the stock piles. This calls for cameras with housings and other features designed

for reliable operation in harsh industrial

environments.

Critical Vessel Monitoring (CVM). There

are several applications where the temperature of a vessel and its contents are critical. The vessels could be used for chemical reactions, liquid heating, or merely storage. For large vessels,

the use of contact temperature sensors

poses problems. One reason could be non-uniform temperatures throughout a vessel and across its surface. This would

require a large number of contact type

sensors, whose installations can become

quite costly.

For most CVM applications, a few IR cameras can image nearly 100% of a vessels surface (Figure 9). Moreover, they

can measure the surface temperature of the CVM to trend and predict when the internal refractory will break down and compromise the mechanical integrity of

the system. If specic regions of interest (ROIs) must be focused on, IR camera rmware (or external PC software) allows

the selection of spot temperature points or areas for measurement.

Again, some variation of the systems

described earlier can be used. Depending

Figure 7. Control room for waste pile processing, and screen capture of the zone monitoring layout, which uses a FLIR IR camera on a pan-tilt mount for re hazard warning.

Remote IR Monitoring

Figure 8. Visible light and IR images of a coal pile – the thermographic image clearly identies a hot spot that is a re about to erupt.

on the application environment, an

explosion proof housing for the camera

may be a requirement. HMI (humanmachine interface) software, such as SCADACAM iAlert from Pivotal Vision, can be used to provide a monitoring overview. This has the ability to combine

all of the camera images into a single spatial representation of the monitored

area – in this case, a attened-out view of the vessel. This view can be updated continuously for a near-real-time

thermographic representation.

Electrical Substation Monitoring. Reliable operation of substations is crucial for

uninterrupted electrical service. Besides lightning strikes and large overloads,

aging equipment and connections are a major cause of infrastructure failures

and service interruptions. Many of these failures can be avoided with eective preventative maintenance monitoring. Often, the temperatures of transformers, breakers, connections, etc. will begin to

creep up before a catastrophic failure occurs. Detection of these temperature increases with IR cameras allows

preventative maintenance operations

Figure 9. CVM monitoring example showing camera locations, network connections, and PC.

1 Computer 2 CAT-6 Ethernet cable with RJ45

connectors

3 Industrial Ethernet switch with PoE 4 ThermoVision™ A320 cameras 5 Industrial process to be monitored,

e.g., a gasier

Chapter 

before an unplanned outage happens.

(See Figure 10.)

The cameras can be installed on a pan/ tilt mounting mechanism to continually

survey large areas of a substation (Figure

11). A few cameras can provide real-time coverage of all the critical equipment

that should be monitored. In addition

to preventative maintenance functions, these cameras also serve as security

monitors for intrusion detection around the clock.

By combining the cameras’ Ethernet and/or wireless connectivity with a web-enabled operator interface, live

images can be transmitted to utility

control rooms miles away. In addition,

trending software can be used to detect dangerous temperature excursions and

notify maintenance personnel via email

and snapshot images of the aected equipment.

These features and functions are already in place at leading utility companies in

the U.S., such as Exel Energy’s “Substation

of the Future.” Companies such as

Exel consider IR monitoring a strategic

investment in automation, which is part of a common SCADA (Supervisory Control And Data Acquisition) platform

for maintenance and security operations.

The most advanced systems provide time-stamped 3-D thermal modeling of critical equipment and areas, plus temperature trending and analysis. A company-wide system of alerts provides alarms on high, low, dierential, and

ambient temperatures within or between

zones in real time.

The previous examples represent just a few applications that can benet from remote IR camera monitoring. A few

other applications where IR temperature monitoring is being used include:

Oil and gas industries (exploration • rigs, reneries, are gas ues, natural gas processing, pipelines, and storage facilities)

Electric utilities (power generation • plants, distribution lines, substations, and transformers)

Figure 10. Visible light and IR images of a substation showing a transformer with excessive temperature.

Remote IR Monitoring

Figure 11. Example pan/tilt mounting system.

Smarter surveillance for a smarter grid

Meet ScadaCam Intelligent Surveillance, the only system in its price range that can automatically perform site patrols, monitor equipment temperature, and scan for security breaches without human supervision.

By combining visual, thermal imaging, and thermographic cameras into a multifunctional operations and security automation tool, ScadaCam can detect, validate, and alarm you of problems that could otherwise result in a major outage – before they occur.

See it in action at www.pivotal-vision.com/tryit

Chapter 

Predictive and preventative • maintenance (continuous/xed

position monitoring of critical

equipment)

Besides these, there are many qualitative

remote monitoring applications where imaging is the predominant feature. For

example, IR cameras can be used as part

of an early warning system for forest

res (Figure 12), detecting blazes before signicant amounts of smoke appear. Another example is using IR imaging to look through condensation vapor that would otherwise obscure an operator’s view of equipment and processes. This is being used in coking plants, veneer mills, and plywood log handling operations, among others (see Chapter 1, Figure 1).

Summary

As noted in the text, IR camera

temperature data may be used for

qualitative monitoring or for quantitative

temperature measurement and control.

In the former, thermal images are

obtained and interpreted based on temperature contrast. It can be used to identify image areas that correlate

to sub-surface details, liquid levels, refractory, etc.

Quantitative measurements generally

require the IR camera to accurately determine the temperature dierence between the target object and its

surroundings. In remote monitoring, this

allows the temperature data to be used

for alarm purposes or to even shut down

equipment. Since temperature changes

slowly in many situations, the near-real-

time data communications of smart IR cameras are more than adequate for alarm and control systems.

Figure 12. Ngaro’s IRIS® Watchman forest re early warning system uses a FLIR IR camera.

Temperature Measurement

for Automated Processes

Temperature Measurement

for Automated Processes

Background

In Chapter  the emphasis was on

specic applications where a single

temperature threshold is programmed

into an IR camera, and when the

threshold is reached an alarm is triggered through a PLC. Multiple

cameras are often required, but viewing an IR cameras’ thermographic image is a secondary consideration – to verify an alarm condition. Chapter 3

focuses on applications where multiple

temperatures within a single camera’s FOV are important, and that information

is used for some sort of process control

function. In these applications, the

camera is typically integrated with other

process control elements, such as a PC or

PLC using third party software and more sophisticated communication schemes.

Typical Camera Measurement Functions

Many IR cameras provide the user with

dierent operating modes that support correct temperature measurements

under various application conditions.

Typical measurement functions include:

Spotmeter•

Area•

Image mask• Delta T• Isotherm• Temperature range• Color or gray scale settings•

The last two are used with the others to

provide a visual indication of the range of temperatures in the camera’s FOV. Generally, spot and area temperatures

tend to be the most useful in monitoring

and control applications, and most

cameras allow multiple spots or areas to be set within the thermographic image.

For example, the FLIR A320 camera

supports up to four spots and four areas.

Cursor functions allow easy selection of

an area of interest, such as the crosshairs of the spot readings in Figure 1. In addition, the cursor may be able to select circlular, square, and irregularly shaped

polygon areas.

Figure 1. IR image of a printed circuit board indicating three spot temperature readings. Image colors correspond to the temperature scale on the right.

The spotmeter nds the temperature

at a particular point. The area function isolates a selected area of an object or

scene and may provide the maximum, minimum, and average temperatures

inside that area. The temperature measurement range typically is

selectable by the user. This is a valuable

feature when a scene has a temperature

range narrower than a camera’s full-

scale range. Setting a narrower range allows better resolution of the images and higher accuracy in the measured

Chapter 3

Figure 2. Gray scale images of a car engine – the left view has white as the hottest temperature and the right view shows black as the hottest.

temperatures. Therefore, images will

better illustrate smaller temperature

dierences. On the other hand, a

broader scale and/or higher maximum temperature range may be needed to

prevent saturation of the portion of the

image at the highest temperature.

As an adjunct to the temperature range selection, most cameras allow a user

to set up a color scale or gray scale to

optimize the camera image. Figure 2

illustrates two gray scale possibilities.

In Figure 1, a so-called “iron scale” was

used for a color rendering. In a manner

similar to the gray scale above, the

hottest temperatures can be rendered as either lighter colors or darker colors.

Another possibility is rendering images

with what is known as a rainbow scale

(Figure 3).

While choice of color scale is often a

matter of personal preference, there may

be times when one type of scale is better than another for illustrating the range of temperatures in a scene.

Figure 3. Rainbow scale showing lower temperatures towards the blue end of the spectrum.

Application Examples

Go/No-Go. In these applications, one or

more temperatures are monitored to

make sure they meet process criteria,

and machinery is shut down or product rejected when a measured temperature

goes above or below the setpoint. A

good example of this is a manufacturer

of automotive door panels that uses

IR cameras to monitor and measure part temperatures prior to a molding procedure.

Temperature Measurement for Automated Processes

This process starts with reinforcing parts

that have been stored in a warehouse.

In either the warehouse or during

transport to the molding line, these

parts can become wet due to moisture condensation or exposure to inclement

weather. If that happens, they may not

reach a high enough temperature in the

molding press and nished panels will be

of poor quality.

The parts go into the press two at a

time from a conveyor where they are sealed together and the nished door

panel is molded into the required shape

for a specic car model. If the parts are wet, this creates steam in the press and

causes mold temperature to be too low.

However, it was found that movement of wet parts on the conveyor causes their temperature to be lower than normal. So, just before the parts go into the press, the conveyor stops and an IR camera makes a non-contact measurement

of their temperature. The diagram in

Figure 4 is typical for this type of quality

control application.

The IR camera’s area tools are applied to

the thermographic image to check for the minimum allowable temperature of the two parts. If either temperature is

below the setpoint (typically, the ambient temperature), then a digital I/O output to

a PLC causes an alarm to be sounded and

Figure 4. Typical Go/No-Go QC inspection system using IR cameras.

1 Computer or PLC 2 CAT-6 Ethernet cable with

RJ45 connectors

3 Industrial Ethernet switches

with ber optic ports 4 Fiber optic cable 5 ThermoVision™ A320 or A325

cameras

6 Industrial process to be

monitored, e.g., items on a

conveyor belt

Chapter 3

the molding line is halted so the parts can

be removed.

For OEMs, preventing bad panels from getting to the end product avoids a

potential loss of business. Warranty replacement of a door panel after an end customer takes possession of the car is an

expensive proposition for the OEM.

The trick is to make sure the camera is measuring the temperature of the parts

and not the oor beneath the conveyor, which is within the camera’s FOV and

typically much cooler. This occurs when

the parts are not in the proper position. A

photoelectric detector tells the PLC when the parts enter the press area; otherwise its ladder logic ignores the alarm output from the camera.

Continuous Process Monitoring.

Temperature is an important variable

in many processes. It can either be an integral part of a process or act as a proxy for something else. The following describes an example that encompasses both of these situations.

Articial ber production typically involves a continuous extrusion process.

Multiple strands may be extruded

simultaneously or, in the case of nonwoven sheets, a web process may be involved. In either case, monitoring

the temperature of the material as it comes out of the extruder can detect strand breakage or material blockage

and backup in the process. Using an IR

camera for unattended monitoring can

catch these malfunctions early, before

a huge mess is created that causes a long machinery outage and costly

production losses. In addition, the actual

temperature readings can be used for trend analysis.

Depending on the application, either the

spot or area measurement functions of

the camera can be used. In the latter case,

it is likely that the application would take

advantage of all the area measurement capabilities – minimum, maximum, and average temperatures of the dened

area. If any of these were to fall outside

the user-dened limits, the application

program running on a PC or PLC could instantly shut down the process machinery.

In one such application, FLIR customized the camera rmware to

allow simultaneous monitoring of up

to 10 dierent areas. Figure 5 shows a monitored area covering six ber strands coming out of the extruder, along with an

alarm setpoint temperature in the upper left corner.

Figure 5. Monitoring of articial bers coming out of an extruder.

As in the case of many remote monitoring applications, the user may choose to route the camera’s analog video to a

control room monitor. For cameras

with an Ethernet connection, digitally

Temperature Measurement for Automated Processes

compressed (MPEG-4) streaming video can be available for monitoring on a PC screen. With FLIR’s A320 camera, images and alarms can be sent to a remote PC via TCP/IP and SMTP (email) protocols.

While a visible light camera may be able to detect broken ber strands, an IR camera can also provide temperature

measurements for trending and statistical

process control (SPC) purposes. In addition, some textile processes create steam or condensation vapors that a visible light camera cannot see through, but an IR camera can. Thus, an IR camera provides multiple functions and is more cost eective.

Data Communications and Software Considerations

Dierent cameras have dierent video frame rates. The frame rate governs how

frequently the thermographic image and its temperature data are updated.

A typical rate might be every 200ms or so. The camera’s digital communications

protocol could create a small amount of additional latency in the update process.

Still, because process temperatures tend to change slowly, collecting temperature data at this rate provides a wealth of

information for quality control purposes.

In many IR cameras there is some sort of serial/socket interface that can be used for communications with the PC

or PLC that is running a control script,

or application. When a system designer

or user is most familiar with PLCs, the

control algorithm can be built around

a virtual PLC created on a PC, which

emulates actual PLC hardware and logic.

In any case, a human-machine interface (HMI) is created to monitor data coming

from the camera. The details described

below are based on FLIR’s A320 camera, but should be representative of most cameras that transmit data over an

Ethernet link.

The only physical interface for digital

data transfer from the FLIR A320 is the Ethernet port. Only TCP/IP is supported,

but the camera should work seamlessly

on any LAN when the proper IP address, netmask, and possibly a gateway is set

up in the camera. The two main ways of controlling the camera are through the command control interface and the resource control interface. Digital image

streaming, data le transfer, and other functionality is provided through the IP services interface. A lot of software

functionality is exposed through software resources. These resources can be reached through the FLIR IP

Resource Socket Service. This is the camera’s resource control (serial/socket)

interface. Independent of the physical

Ethernet interface, it is possible to access

the camera system using TCP/IP with

telnet, ftp, http, and FLIR Resource Socket Services (among others).

Most PLCs provide serial/socket interfaces for Ethernet. One example is AllenBradley’s EtherNet/IP Web Server Module (EWEB for short). Another example is HMS Industrial Network’s Anybus X-Gateway Ethernet interface module, which can convert this serial socket interface to many industrial network protocols, such as EtherNet I/P, Modbus-TCP, Pronet, Ethernet Powerlink, EtherCAT, FLNet., etc.

Camera setup and data acquisition is normally done directly through the FLIR IR

MONITOR and IP CONFIG software running on a PC. Afterward, the camera can be

Chapter 3

connected on the network for continuous

monitoring and data logging via PC or PLC control. Typically, the telnet protocol,

accessed by the Windows® PC running

the application program, is used to query

the camera for data. This protocol is also

available for most PLCs. In either case, this takes place though the camera’s Resource Socket Services. (Command syntax is contained in the camera’s ICD manual; a few examples are listed in Appendix D.)

The system designer or FLIR would create the message instructions that allow the PLC to query the camera for temperature data and thermographic

images in the same way it is done with PC

control. Alternatively, the PLC can hold

the Ethernet port open and call for the camera to continuously output data to this port at the maximum rate possible. In

either case, alarm functions and decision-

making is performed by the application

program running on the PLC (or PC if applicable). (See Figure 6.) Typically,

temperatures and images collected for trend analysis and statistical process control purposes are stored on a separate

server connected to the network, which

is running transaction manager software for downloading and storing data.

1 Computer, PLC, and/

or transaction manager

server

2 CAT-6 Ethernet cable

with RJ45 connectors

3 Industrial Ethernet

switches with ber optic

ports

4 Fiber optic cable

 Wireless access points

6 CAT-6 Ethernet cable

with RJ45 connectors.

Powering the camera

using PoE (Power over Ethernet)

7 Industrial Ethernet

switch

8 ThermoVision A320

cameras monitoring a process or other target objects

Figure 6. Generalized IR machine vision system and its communications network

Temperature Measurement for Automated Processes

Figure 7. Example of a control and data acquisition option for IR cameras

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Chapter 3

For system developers who are writing or modifying code with Visual Basic, C++, etc. for customized applications running on a PC, there are a few options. FLIR’s Researcher package supports OLE-2,

the Microsoft standard for linking and embedding data between applications. Image and temperature data can be linked from Researcher into other

compliant applications, such as Excel. The linked data updates automatically, so if a temperature value changes in Researcher

it will automatically change in the linked

application. In addition, Researcher provides an automation interface that

can be used to control the software using

Visual Basic or VBA. Other o-the-shelf options for OLE control include National Instruments’ MATLAB and LabVIEW®. However, none of the aforementioned are OPC (OLE for Process Control) compatible.

There are other out-of-the-box solutions

that do not require the writing of

application source code. One of these is IRControl from Automation Technology, GmbH. IRControl simplies automated

processing of complex tasks with its

built in Automation Interface based on

Microsoft® COM/DCOM. All essential

measurement, analysis, and control

functions for FLIR IR cameras are directly programmable using macro commands. This allows the execution of control scripts automatically based on digital

input events. In addition, IRControl

accepts remote control commands sent

over an RS-232 link. Therefore, remote

control of IRControl by other computers

or PLCs is greatly simplied. The software also includes a comprehensive report

generator.

Summary

A variety of control and data acquisition options are available for IR cameras (see Figure 7). They are similar to those used with visible light cameras that are employed in machine vision and automation systems. IR cameras provide the added advantage of accurate non-

contact temperature measurements within a single instrument.

Combining Machine Vision and

Temperature Measurement

Combining Machine Vision and Temperature Measurement

Background

Traditionally, visible light cameras have been a mainstay in machine vision

systems used for automated inspection and process control. Many of these systems also require temperature measurements to assure product quality.

In numerous cases, an IR camera can

supply both an image of the product and critical temperature data. If the

application will not benet from thermographic images and non-contact temperature measurements, then a visible light camera is certainly less expensive. If the opposite is true, then an

IR camera should be considered by the system designer.

As the sophistication of IR cameras continues to increase, along with associated hardware and software, their

use in automated systems is growing rapidly. Because of their combined imaging and temperature measurement

capabilities, they can be very cost eective. The main impediment to their wider usage is system designers’ lack

of familiarity with IR camera features

and the related standards, systems, and

software that support them. This chapter supplies a good deal of that information.

Machine Vision Applications

As in the case of visible light cameras,

thermographic cameras and their

associated software can recognize the size, shape, and relative location of target objects (i.e., they can do pattern

matching). Moreover, the electronics in newer IR cameras provide fast signal processing that allows high video frames rates (60Hz or higher) to capture relatively fast-moving parts on a production line. Their A/D converters combine short integration times with 14- to 16-bit resolution, which is critical for properly characterizing moving targets or targets

whose temperatures change rapidly.

Figure 1. Results of automated inspection of ICs on a circuit board

One example of the latter is automated

inspection of operating ICs on a circuit

board (Figure 1). In some cases, this involves overload testing in which an

IC is subjected to a current pulse so its

heat loading can be characterized. In one such case the IC is forward and reverse biased with current levels outside of

design limits using a pulse that lasts

800ms. The IR camera captures images

during and after the current pulse to

characterize temperature rise and fall. With a 60Hz frame rate, a new frame can be captured about every 17ms. In such a system nearly 50 frames can be captured during the 800ms pulse, and many more

Chapter 4

are typically captured afterward to reveal

heat dissipation characteristics.

In other applications of this sort, a good

image can be stored and compared to

the inspection image by using pixel-bypixel subtraction. Ideally, the resulting image would be entirely black, indicating no dierence and a good part. Areas with excessive temperature dierences indicate a bad part, making it very easy to

discern unwanted dierences.

There are many other applications

where the combination of non-contact

temperature measurements and imaging

at high frame rates is extremely valuable.

Some automated systems where IR cameras are already being used include:

Automotive part production and •

assembly lines

Steel mill operations, such as slag •

monitoring and ladle inspection

Casting, soldering, and welding of •

metals and plastics Food processing lines• Product packaging•

Non-destructive testing, like sub-• surface detection of voids in molded

parts Electric utility equipment monitoring•

R&D, prototyping, and production in •

the electronics industry

An interesting automotive example is

monitoring the temperature distribution

of a pressure casting mold for a safetycritical part (Figure 2). Prior to installation of the IR machine vision system, the manufacturer was doing 100% inspection using an X-ray system to reveal

subsurface imperfections. It was not

practical to do this as an inline procedure,

so the X-rays were taken a few hours after part production. If the X-rays showed a signicant problem in parts coming from a particular mold, this information was

relayed to the production area so that mold temperatures could be adjusted. This was a lengthy and costly process that often resulted in high scrap rates. With

the IR camera system, the mold operator

can immediately check and adjust the temperature distribution of the mold.

Figure 2. Pressure casting mold and its temperature distribution – an IR camera image is used by the operator to adjust the mold temperatures as required to produce good parts.

Enabling Technology

Data communications are the backbone

of modern industrial SCADA, PLC, HMI’s,

and Machine Vision systems. Ethernet has become the de facto standard for such

systems. Considering this, the features of

Combining Machine Vision and Temperature Measurement

IR cameras that make for practical use in

machine vision applications are Gigabit Ethernet (GigE) connectivity, GigE Vision™ compliance, a GenICam™ interface, and

a wide range of third party software that supports these cameras. There are other hardware features that are also important.

Generally, ultra-high detector resolutions

are not needed in the targeted

applications, so a typical focal plane array (FPA) would be 320x240 pixels. Nevertheless, outputting a 16-bit image stream of these 76,800 pixels at a 60Hz frame rate amounts to about 74Mb/

sec. While this is much slower than a

1000baseT Ethernet system is capable of,

multiple cameras may be connected and

there may be a lot of other trac on the

network between image transmissions.

To speed up image transfers, data analysis and decision-making must take

place outside the camera and is one of the reasons why there is a good market

for third-party thermographic software.

The other reason is that most machine

vision systems are custom designed for specic production processes. Of course, IR camera manufacturers supply various types of software to support their

products and facilitate application in these systems.

The goal of the GigE Vision technical standard is to provide a version of GigE

that meets the requirements of the

machine vision industry. One of the industry objectives is the ability to mix and match components from various

manufacturers that meet the standard.

Another is relatively inexpensive accessories, such as cabling, switches, and network interface cards (NICs) as well

as the ability to use relatively long cable

runs where required.

The GigE Vision standard, which is based on UDP/IP, has four main elements:

A mechanism that allows the •

application to detect and enumerate

devices and denes how the devices obtain a valid IP address.

GigE Vision Control Protocol (GVCP) • that allows the conguration of detected devices and guarantees

transmission reliability.

GigE Vision Streaming Protocol (GVSP) • that allows applications to receive information from devices.

Bootstrap registers that describe the • device itself (current IP address, serial number, manufacturer, etc.).

With GigE capabilities and appropriate software, an IR machine vision system

does not require a separate frame

grabber, which was typically the case with visible light cameras in the past. In eect, the GigE port on the PC is the frame grabber. Older visible light cameras that have only analog video outputs (NTSC and PAL) are limited to much lower frame rates and video monitor observations. By using GigE, an IR vision system not only has higher frame rates, but can be monitored remotely over

much greater distances compared to local processing and transmitting data

over USB, Firewire, CameraLink, etc. In addition, Ethernet components are inexpensive compared to frame-grabber

cards and related hardware.

A GigE Vision camera typically uses an NIC, and multiple cameras can be connected on the network. However, the drivers supplied by NIC manufacturers

Chapter 4

use the Windows or Linux IP stack, which may lead to unpredictable behavior,

such as data transmission delays. By

using more ecient dedicated drivers compatible with the GigE Vision standard, the IP stack can be bypassed

and data streamed directly to memory at

the kernel level of the PC system. In other words, Direct Memory Access (DMA) transfers are negotiated, which also eliminates most CPU intervention. Thus a near-real-time IR vision system is created in which almost all of the CPU time is

dedicated to processing images.

To make sure a camera is GigE Vision compliant, look for the ocial stamp (shown in Figure 3) that can only be

applied if the camera conforms to the standard.

Figure 3. Ocial trademark for GigE compliant products

GenICam compliance should also

be considered for an IR camera.

GenICam compliance makes it easier for developers to integrate cameras into their IR vision system. The goal of the GenICam standard is to provide

a generic programming interface for

all kinds of cameras. No matter what interface technology (GigE Vision, Camera Link, 1394, etc.) is used, or what camera features are being implemented,

the application programming interface

(API) should be the same. The GenICam

standard consists of multiple modules and the main tasks each performs are:

GenApi: conguring the camera• Standard Feature Names: •

recommended names and types for common features

GenTL: transport layer interface, •

grabbing images

The GenApi and Standard Feature Names modules are currently part of the standard module only. GenTL should be nished soon.

Common tasks associated with IR

cameras in machine vision systems include conguration settings, command and control, processing the image, and

appending temperature measurement results to the image data stream. In

addition, the camera’s digital I/O can be used to control other hardware, and there are triggering and synchronization functions associated with real-time data acquisition. GigE Vision makes hardware independence possible, while GenICam

creates software independence. For

example, in a system with IR cameras

compliant in both and connected to a

GigE network, virtually any application

program can command a camera to

send a 60Hz stream of images that can

be easily captured without dropping frames and losing important data. This information can be processed for alarm

functions, trend analysis and statistical

process control.

Third Party Software Expands

Applications

By adhering to the standards described

above, IR camera manufacturers are making it easier for developers to integrate their cameras into vision

systems with a broad array of functions

Combining Machine Vision and Temperature Measurement

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(Figure 4). Camera manufacturers also supply a variety of software products to ease integration tasks. For example, the FLIR A325 comes with three packages that

run on a PC controller:

IP Conguration utility – nds cameras • on the network and congures them

IR Monitor – displays images and •

temperature data on up to nine cameras simultaneously

AXXX Control and Image interface • – low-level descriptions of how to communicate with the camera, including image formats and C-code

examples

In addition, optional software developer toolkits are available (FLIR SDK, LabVIEW SDK, Active GigE SDK from A&B Software, etc.) for those creating source code for

custom applications within programming

environments such as Visual Basic, C++, Delphi, etc. However, the strength of a camera like the A325 is its ability to

interface with third party software that

eliminates or minimizes the need to write source code. For example, National Instrument’s Vision Builder for Automated Inspection is a congurable package for building, benchmarking, and deploying machine vision applications (Figure

5). It does not require the user to write program code. A built-in deployment

interface facilitates system installation

Figure 4. IR cameras can be used in a broad array of applications

Chapter 4

and includes the ability to dene complex pass/fail decisions, control digital I/O, and communicate with serial or Ethernet devices, such as PLCs, PCs, and HMIs. Similar features are available in Common Vision Blox, a Stemmer Imaging product that contains hardware- and language-independent tools and libraries

for imaging professionals.

By using third party software to get much

of the analytics, command, and control

functions out of the camera and onto a

PC, application possibilities are greatly expanded. One possibility is creating a mixed camera system. For instance, IR

cameras could be used to supply thermal

images and temperature data, while visible light cameras could provide “white

light” color recognition.

The food processing industry is one in

which higher level analytics are used with IR cameras for automated machine vision applications. A broad area of applications where IR vision systems excel is in 100%

inspection of cooked food items coming

out of a continuous conveyor oven. A

primary concern is making sure the

items have been thoroughly cooked, which can be determined by having the camera measure their temperature, which is illustrated in Figure 6 for

hamburger patties. This can be done by

dening measurement spots or areas

corresponding to the locations of burgers

as they exit the oven. If the temperature of a burger is too low, the machine vision program logic not only provides an alarm, but also displays an image to the oven operator to show the specic burger that should be removed from the line. As in other applications, minimum, maximum, and average temperatures can be collected for specic burgers or the FOV as a whole and used for trending and

SPC purposes.

Figure 6. IR machine vision image for checking hamburger doneness by measuring temperature

In another example involving chicken tenders, temperature is again used to

check for proper cooking. The pieces

come out of the oven and drop onto another conveyor in more or less random locations (Figure 7). The operator can

use the thermographic image to locate undercooked items within the randomly

spaced parts and then remove them from the conveyor.

In the production of frozen entrées, IR machine vision can use pattern

recognition software to check for proper

lling of food tray compartments.

Find any number of edges

Set up coordinate systems

Find and match patterns

Acquire with IEEE 1394 and GigE cameras

Detect and measure objects

Calibrate measurements to real-world units

Perform advanced geometric analysis

Find and measure straight edges

Find circular edges

Make caliper distance measurements

Make pass/fail decisions

Read text (OCR)

Communicate with external devices suc

as PLCs

Measureintensity

Read 1D and 2D bar codes

Figure 3. Examples of the many functions available in Vision Builder for automated inspection

Combining Machine Vision and Temperature Measurement

Similarly, it can be used for 100% inspection of the heat-sealed cellophane cover over the nished entrée. An added

function could be laser marking of a

bad item so it can be removed at the

inspection station.

Summary

IR machine vision and temperature

measurements can be applied to an

innite number of automated processes. In many cases, they provide images and information that are not available with visible light cameras, and they

also complement white light images where the latter are required. IR cameras

like the FLIR A325 provide a stream of digitized IR images at fast frame rates for relatively high-speed processes, which can be transmitted over GigE networks to remote locations. Compliance with GigE Vision and GenICam standards means

that such cameras can be integrated

with a wide variety of similarly compliant

equipment and supported by a broad range of third party software. Trigger and

synchronization capabilities allow them to control, or be controlled by, a host of other types of equipment. The availability of wireless and beroptic line adapters

allow these cameras to be used almost

anywhere, including over long distances.

Figure 7. An IR temperature measurement and thermographic image are used to locate undercooked chicken tenders and stop the line so bad parts can be removed.

Chapter 

Real-Time Control Issues

Background

Real-time control is an important issue in most IR machine vision systems used for

automated temperature monitoring and

inspection. Having said that, it should be noted that real time tends to be a relative term, the measure of which varies with

the application and user requirements. In

some applications, users would consider a response time of 100 milliseconds to meet their denition of real-time. On the other hand, many electronic events are extremely fast or short-lived, and a one-microsecond response might be needed. As mentioned in earlier chapters,

process temperatures tend to change

relatively slowly, so an IR machine vision

system that can update images and

temperatures every 10-100ms, or even less frequently, may be adequate.

Hardware and Software Platform Considerations

In most cases, a PC with a Microsoft Windows operating system (OS) isn’t well suited for controlling fast, real-time

applications. Windows is referred to as a

non-deterministic OS because it typically cannot provide predictable response

times in critical measurement and control

situations. Therefore, the solution is to

link the PC to a system that can operate

autonomously and provide rapid,

predictable responses to external stimuli.

Deterministic applications (those intended to be event driven) are

controlled better with systems based on an embedded microprocessor and/

or digital signal processor (DSP) that has a dierent type of OS – or perhaps a special version of Windows other than

the ones typically found on a home or

Figure 1. PLCs are a good choice for creating deterministic (event-driven) systems, supported by a PC that is used for data trending.

GPIO or PLC

Real-Time Control Issues

oce PC. Often, a system based on a PLC with 115VAC control I/O is much more appropriate for real-time applications (Figure 1). PLC processors are designed

to operate with deterministic control

loops, and the 115VAC control signals are

inherently immune to noisy industrial

environments.

If the required response time is long

enough, and there are other reasons

to use one of the familiar Windows

operating systems, keep in mind that special steps are needed to improve its

data polling methodology. In a polled

system, the PC checks many devices to see if they’re ready to send or receive

data. In the context of data acquisition

from an Ethernet-based IR camera, this typically involves reading values from a data stream. In a Windows-based PC, the time between polled readings is scheduled by Windows, so it’s nondeterministic. In other words, the time at

which Windows will initiate an operation cannot be known precisely. Its operation depends on any number of system

factors, such as computer speed, the OS, programming languages, and application code optimization.

Polling can be appropriate with slower, less time-sensitive operations. In contrast, event-driven programming schemes are less dependent on OS timing and tend

to reduce latency problems. They can be used to create more deterministic

systems that collect discrete data values

that are closely related to the physical phenomena being represented.

Creating such a system within a

Windows OS environment generally

requires writing program code using

Visual C/C++ , Visual Basic, etc. Using

these tools, a programmer can take advantage of Windows events and

messaging functionality to create a more deterministic application that

runs relatively fast and provides tight

control. Rather than constantly polling to

determine if data is ready for collection, such programs can use the PC’s CPU for additional tasks, such as database or network access, until interrupted

by the automation system hardware.

As discussed in Chapter 4, there are software developer kits that take some of the work out of these tasks, and third

party software packages can eliminate

or minimize the need to write program code. An example is illustrated in Figure 2.

Figure 2. Third party software provides powerful control and analytic tools for IR machine vision systems without writing program code.

Data Communication Latencies

Hardware and data communications

have signicant eects on system

response time. The Ethernet interfaces on many IR cameras allow communication

distances of 4000 feet or more. Wireless and beroptic adapters and hubs can extend the scope of the network even more. Networked systems require the

Chapter 

installation of one or more NICs in the PC and conguring its OS for network

support. These requirements are easily

and economically met with Ethernet, TCP/IP, and Windows, as described in Chapter 3.

A functional drawback of Ethernet-based systems concerns real-time control. Like Windows, Ethernet is a non-deterministic

system that in many applications

precludes fast, real-time process control. This can become even more of

an issue when the World Wide Web is

involved. Again, there are work-arounds to minimize inherent weaknesses. As

mentioned in Chapter 4, drivers supplied by NIC manufacturers use the Windows or Linux IP stack, which may result in data

transmission delays. By using dedicated

drivers compatible with the GigE Vision standard, data can be streamed directly to memory using a DMA transfer.

Since older communication protocols

(RS-232, 422, 485, etc.) are even slower,

Ethernet is still the protocol of choice

in most IR machine vision systems. The digitized streaming video from FLIR’s A325 camera allows near-real-time

data acquisition of thermal images

and temperature data – provided the

1 Computer, PLC, and/

or transaction manager

server

2 CAT-6 Ethernet cable

with RJ45 connectors

3 Industrial Ethernet

switches with ber optic

ports

4 Fiber optic cable

 Wireless access points

6 Industrial Ethernet

switch

7 ThermoVision A320 or

A325 cameras monitoring

a process or other target objects

Figure 3. Generalized IR machine vision system and its communications network

Real-Time Control Issues

PC has the appropriate NIC driver and application program. Network adapters for beroptic and wireless connectivity can extend Ethernet’s scope (Figure 3). Other hardware timing issues can be minimized by using direct-wired digital I/O and triggering between individual cameras, PLCs, etc. Analog video (NTSC and PAL) for conventional image

monitoring is probably most applicable

to qualitative applications where timing

is not critical.

IR Camera Hardware and Firmware Issues

Thermal Time Constants for Cooled and Uncooled IR Cameras. In general, time

constant refers to the time it takes for a sensing element to respond to within

63.2% of a step change in the state of a target that is being sensed (Figure

4). In IR sensing and thermography,

the thermal time constant of an IR

camera’s detector is a limiting factor in

instrument performance as it relates to response time.

100%

80%

63%

0 1 2

Thermal Time Constants

Step Change in Target State

Figure 4. Thermal time constant concept showing an integral number of time constants on the X-axis.

Older IR cameras have response times similar to the human eye, so they are

unsuitable for capturing thermal images

of fast moving objects or those with rapidly changing temperatures. Newer IR cameras have detectors and digital

electronics with response times in the

sub-millisecond region. Cooled quantum detectors are very sensitive and very fast (sub-microsecond response times),

but their bulkiness and cost tends to rule them out of many automation

applications. In addition, quantum detectors have response curves with detectivity that varies strongly with IR wavelength. FLIR has made recent improvements to its uncooled

broadband microbolometer detectors

and associated A/D converters so they

can continuously output images with embedded temperature data at a

60Hz rate. This is satisfactory for most

temperature monitoring and IR machine

vision applications.

Temperature Measurement Range.

The overall temperature range of an

IR camera is primarily a function of its detector and calibration. Camera

electronics, which include calibration functions, can handle wide variations in absolute detector sensitivities. For example, the FLIR A325’s overall measurement range is divided into userselectable temperature scales that have a measurement accuracy of ±2°C (±3.6°F) or ±2% of reading:

–20°C to + 120°C (–4°F to +248°F)• 0°C to +350°C (32°F to +662°F)• Optionally, 250°C to +1200°C (482°F to •

2192°F)

This is a valuable feature when a scene

has a temperature range narrower than

Chapter 

a camera’s overall range. Selecting a

narrower scale allows better resolution of the images and higher accuracy in

the measured temperatures. Therefore,

the images will better illustrate smaller

temperature dierences. On the other hand, a broader scale and/or higher

maximum temperature range may be

needed to prevent saturation of the

portion of the image at the highest temperature.

It’s important to understand how the camera’s calibration and temperature

measurement processes aect its response time. IR cameras measure

irradiance, not temperature, but the

two are related. When an IR camera

is thermographically calibrated, it

can measure temperatures based on

standard blackbody radiances at specic temperatures. As will be discussed later, the emissivity of the target object being measured is vital to achieving accurate temperature readings. (Emissivity or emittance is the radiative properties of an object relative to a perfect blackbody.)

When an IR camera is calibrated at the

factory, calibration factors are stored internally as a table of values based on the camera’s A/D counts from the

temperature/radiance measurements of a standard blackbody. When the system

makes a measurement in an application, it takes the digital value of the signal at a given moment, goes into the appropriate calibration table, and calculates temperature. Before the nal result is presented, due consideration is given to other factors, like emissivity of the target objects, atmospheric attenuation, reected ambient temperature, and the camera’s ambient temperature drift.

Figure 5. Creating a temperature scale narrower than the camera’s full range improves image resolution and may improve defect detection.

As an adjunct to major temperature scale selections, most IR cameras allow a user

to set up a color scale or gray scale for a

temperature range that’s even narrower (Figure 5). This should be done where practical, not only because of improved image resolution, but also because of response time considerations. A narrower temperature range can reduce the A/D converter’s processing load and overall

response time of the system.

Another complexity is the fact that each individual pixel in the camera’s

focal plane array has a slightly dierent

gain and zero oset. To create a useful thermographic image, the dierent

gains and osets must be corrected

to a normalized value. This multi-step

calibration process is performed by the

camera rmware (Figure 6). The nonuniformity correction (NUC) factors are

also stored in a table.

IR cameras also have dierent

measurement modes: spotmeter and area measurements in the case of the

FLIR A320 Series. The spotmeter nds the

temperature at a particular point whereas the area function isolates a selected area

of an object or scene. In the latter case,

Real-Time Control Issues

camera rmware nds the minimum and

maximum temperatures and calculates

the average temperature inside the area selected. Clearly, more processing time is required for area measurements,

particularly if multiple areas are selected. This also means that more data is being

transmitted over a machine vision system’s communications network, along

with more latency.

Emissivity Calibration. Earlier, it was

pointed out that accurate temperature

measurements on a specic object

require the emissivity value for that object. In eect, this adjusts the factory

calibration that is based on a perfect

blackbody having an emissivity value of

1.0. This adjustment consumes processor time. To avoid this, the FLIR A325 uses a global emissivity value (input by the user) for the camera’s entire FOV. Normally, this isn’t a problem for machine vision applications and it avoids the time required to apply non-global emissivity values on the y. Instead, the application

program is set up to make decisions

Figure 6b. Final steps in IR camera’s NUC process

Signal SignalWithout any correction

First correction step

Radiation

–20°C+12 0°C

+20°C

Radiation

–20°C +120°C

+20°C

Signal Signal

Third correction, Non-Uniformity Correction (NUC)

After NUC

Radiation

–20°C+12 0°C

+20°C

Radiation

–20°C +120°C

+20°C

Figure 6a. First step in detector non-uniformity correction (NUC) performed by IR camera rmware

Chapter 

based on the temperature value of a target area compared to a standard value or compared to the target’s surroundings.

While this may not be accurate in an

absolute sense, it is the relative dierence

that is most important.

If the system developer wants accurate

temperature measurements on dierent

objects with dierent emissivities (e.g., for PC board inspections), then he/she must create an emissivity map for the camera’s FOV. This cannot be done with

the data coming out of a camera using

a global emissivity value. To create an emissivity map, the developer will need to write some program code, typically by using the FLIR Software Developers Kit. The routine that’s developed sets up

the system to read the FLIR proprietary

stream of data coming out of the A/D converter and applies emissivity values to it. This creates an emissivity map that covers selected areas or spots within the FOV.

Important Thermographic Principles

As alluded to above, there must be

a temperature dierence between a target object and its surroundings in order to create a useful thermographic

image. In most situations, the user also needs a measurement of this relative

temperature dierence for decision

making, either automatically or by a machine operator. However, there are a

few ambient conditions that may obscure the temperature dierence.

In addition to emitting radiation, an

object reacts to incident radiation from its surroundings by absorbing and reecting a portion of it or by allowing

some of it to pass through (as through a

lens). Therefore, the maximum radiation

that impinges on an IR camera lens aimed at an object comes from three sources:

(1) the object’s inherent temperature without inuence from its surroundings, (2) radiation from its surroundings that was reected onto the object’s surface, and (3) radiation that passed through the

object from behind. This is known as the

Total Radiation Law (see below). However,

all these radiation components become attenuated as they pass through the atmosphere on their way to the camera lens. Since the atmosphere absorbs

part of the radiation, it also radiates

some itself.

Total Radiation Law

1 = α + ρ + τ.

The coecients α, ρ, and

τ describe an object’s

incident energy absorption

(α), reection (ρ), and

transmission (τ).

Figure 7. Total Radiation Law

The role of emissivity in distinguishing an object’s temperature from its surroundings was discussed above. As alluded to earlier, it’s wise to take precautions that prevent reected energy

from impinging on the target. Corrections for atmospheric attenuation are normally

built into the camera rmware. Still,

other gases and hot steam between the target and the camera lens can make

measurements impossible, or at least inaccurate. Similarly, an object that is transparent to IR wavelengths may result

in the camera measuring the background behind it or some combination of the object and the background.

Real-Time Control Issues

In the last two cases, spectral lters that are selective at specic wavelengths can help. Certain lters can make an

otherwise opaque gas appear transparent or a transparent object appear opaque

over the appropriate IR band (Figure 8).

Summary

IR cameras used in machine vision and

other automation systems are analogous

to visible light cameras in similar systems. White light cameras have optical issues that must be managed, whereas IR cameras have thermographic issues to resolve. In both cases, achieving real-time

(or near-real-time) response requires

thoughtful selection of the controller and careful design of the application program. Third party software can

provide out-of-the-box program development tools that eliminate or minimize the need to write program code. Generally, there are no perfect solutions – developing an automated machine vision system, whether based on visible light or IR, usually involves

compromises of one sort or another. The camera manufacturer can be a

great source of help in developing

these systems.

Filter Adaptation

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0

Wavelength, µm

Transmission %

3.45µm NBP lter

Polyethylene transmission

Resulting transmission

Figure 8. Application of a narrow bandpass (NBP) lter to achieve nearly complete absorption and high emittance (green curve) from polyethylene lm, allowing its temperature measurement.

Appendix A

Glossary

absorption (absorption factor). The

amount of radiation absorbed by an

object relative to the received radiation. A number between 0 and 1.

ambient. Objects and gases that emit

radiation towards the object being measured.

atmosphere. The gases between the

object being measured and the camera,

normally air.

autoadjust. A function making a camera

perform an internal image correction.

autopalette. The IR image is shown with

an uneven spread of colors, displaying

cold objects as well as hot ones at the same time.

blackbody. Totally non-reective object. All its radiation is due to its own

temperature.

blackbody radiator. An IR radiating device with blackbody properties used to

calibrate IR cameras.

calculated atmospheric transmission. A transmission value computed from the temperature, the relative humidity of the air, and the distance to the object.

cavity radiator. A bottle shaped radiator with an absorbing inside, viewed through

the bottleneck.

color temperature. The temperature for which the color of a blackbody matches a

specic color.

conduction. The process that makes heat spread into a material.

continuous adjust. A function that

adjusts the image. The function works

all the time, continuously adjusting

brightness and contrast according to the image content.

convection. The process that makes hot air or liquid rise.

dierence temperature. A value that is

the result of a subtraction between two

temperature values.

dual isotherm. An isotherm with two

color bands instead of one.

emissivity (emissivity factor). The amount of radiation coming from an object compared to that of a blackbody.

A number between 0 and 1.

emittance. Amount of energy emitted

from an object per unit of time and area

(W/m).

estimated atmospheric transmission.

A transmission value, supplied by a user,

replacing a calculated one.

external optics. Extra lenses, lters, heat

shields etc. that can be put between the camera and the object being measured.

lter. A material transparent only to some of the infrared wavelengths.

FOV. Field of view: The horizontal angle that can be viewed through an IR lens.

FPA . Focal plane array: A type of IR

detector.

graybody. An object that emits a xed

fraction of the amount of energy of a

blackbody for each wavelength.

IFOV. Instantaneous Field Of View: A

measure of the geometrical resolution of an IR camera.

Glossary

image correction (internal or external).

A way of compensating for sensitivity dierences in various parts of live images and also of stabilizing the camera.

infrared. Non-visible radiation, with a wavelength from about 2–13 µm.

IR. Infrared.

isotherm. A function highlighting those

parts of an image that fall above, below,

or between one or more temperature

intervals.

isothermal cavity. A bottle-shaped

radiator with a uniform temperature

viewed through the bottleneck.

Laser LocatIR. An electrically powered

light source on the camera that emits

laser radiation in a thin, concentrated

beam to point at certain parts of the object in front of the camera.

laser pointer. An electrically powered

light source on the camera that emits

laser radiation in a thin, concentrated

beam to point at certain parts of the object in front of the camera.

level. The center value of the temperature scale, usually expressed as a signal value.

manual adjust. A way to adjust the

image by manually changing certain parameters.

NETD. Noise equivalent temperature dierence: A measure of the image noise level of an IR camera.

noise. Undesired small disturbance in the

infrared image.

object parameters. A set of values

describing the circumstances under

which the measurement of an object

was made and the object itself (such as emissivity, ambient temperature, distance, etc.)

object signal. A non-calibrated value

related to the amount of radiation

received by the camera from the object.

palette. The set of colors used to display an IR image.

pixel. A picture element. One single spot

in an image.

radiance. Amount of energy emitted from an object per unit of time, area, and angle (W/m/sr).

radiant power. Amount of energy

emitted from an object per unit of

time (W).

radiation. The process by which electromagnetic energy is emitted by an object or a gas.

radiator. A piece of IR radiating

equipment.

range. The current overall temperature

measurement limitation of an IR camera.

Cameras can have several ranges,

which are expressed as two blackbody temperatures that limit the current calibration.

reference temperature. A temperature which the ordinary measured values can

be compared with.

reection. The amount of radiation

reected by an object relative to the received radiation. A number between 0 and 1.

Appendix A

relative humidity. Percentage of water

in the air relative to what is physically possible. Air temperature dependent.

saturation color. The areas that contain

temperatures outside the present level/

span settings are colored with the saturation colors. The saturation colors

contain an “overow” color and an

“underow” color. There is also a third red

saturation color that marks everything

saturated by the detector indicating that the range should probably be changed.

span. The interval of the temperature scale, usually expressed as a signal value.

spectral (radiant) emittance. Amount of

energy emitted from an object per unit of

time, area, and wavelength (W/m/µm).

temperature range. The current overall

temperature measurement limitation of

an IR camera. Cameras can have several

ranges. They are expressed as two

blackbody temperatures that limit the current calibration.

temperature scale. The way in which an IR image currently is displayed. Expressed

as two temperature values limiting

the colors.

thermogram. Infrared image.

transmission (or transmittance) factor.

Gases and materials can be more or less

transparent. Transmission is the amount

of IR radiation passing through them. A number between 0 and 1.

transparent isotherm. An isotherm showing a linear spread of color, instead of covering the highlighted parts of

the image.

visual. Refers to the video mode of an IR camera as opposed to the normal,

thermographic mode. When a camera is

in video mode it captures ordinary video images, while thermographic images are

captured when the camera is in IR mode.

Thermographic

Measurement Techniques

Thermographic Measurement Techniques

Introduction

An infrared camera measures and images

the emitted infrared radiation from an object. The fact that radiation is a function of object surface temperature makes it possible for the camera to calculate and display this temperature.

However, the radiation measured by the

camera does not only depend on the temperature of the object but is also a

function of the emissivity. Radiation also

originates from the surroundings and is reected in the object. The radiation from the object and the reected radiation will also be inuenced by the absorption of the atmosphere.

To measure temperature accurately, it

is therefore necessary to compensate for the eects of a number of dierent

radiation sources. This is done on-

line automatically by the camera. The

following object parameters must, however, be supplied for the camera:

The reected temperature• The distance between the object and •

the camera

The relative humidity• The emissivity of the object•

The most important object parameter to

set correctly is the emissivity which, in short, is a measure of how much radiation is emitted from the object, compared to

that from a perfect blackbody.

Normally, object materials and surface treatments exhibit emissivity ranging from approximately 0.1 to 0.95. A highly

polished (mirror) surface falls below 0.1, while an oxidized or painted surface has much higher emissivity. Oil-based paint, regardless of color in the visible spectrum,has an emissivity of over 0.9

in the infrared. Human skin exhibits an

emissivity close to 1. Non-oxidized metals represent an

extreme case of almost perfect opacity

and high spectral reexivity, which does not vary greatly with wavelength. Consequently, the emissivity of metals is low – only increasing with temperature. For non-metals, emissivity tends to be

high and decreases with temperature.

Finding the Emissivity of an Object Using a Thermocouple

Select a reference point and measure its temperature using a thermocouple.

Alter the emissivity until the temperature

measured by the camera agrees with the thermocouple reading. This is

the emissivity value of the reference object. However, the temperature of the

reference object must not be too close to the ambient temperature for this to work.

Finding the Emissivity of an Object Using Reference Emissivity

A tape or paint of a known emissivity

should be put onto the object. Measure the temperature of the tape/paint using

the camera, setting emissivity to the correct value. Note the temperature. Alter the emissivity until the area with the unknown emissivity adjacent to the tape/

paint has the same temperature reading.

The emissivity value can now be read.

The temperature of the reference object must not be too close to the ambient temperature for this to work either.

Appendix B

Reected Temperature Parameter

This parameter is used to compensate for the radiation reected in the object and the radiation emitted from the atmosphere between the camera and the object.

If the emissivity is low, the distance very long, and the object temperature relatively close to that of the reected object, it is important to

set and compensate for the reected temperature correctly.

Distance Parameter

This is the distance between the object and the front lens of the camera.

This parameter is used to compensate for the fact that radiation is being absorbed between the object and the camera and the fact that transmittance drops with distance.

Relative Humidity Parameter

The camera can also compensate for the fact that transmittance is somewhat

dependent on the relative humidity of the atmosphere. To do this, set the relative humidity to the correct value. For short distances and normal humidity, the relative humidity can normally be left at a default value of 50%.

Other Parameters

In addition, some cameras and analysis

programs from FLIR Systems allow you to compensate for the following parameters:

Atmospheric temperature – the •

temperature of the atmosphere between the camera and the target

External optics temperature – the •

temperature of any external lenses or windows used in front of the camera

External optics transmission – the •

transmission of any external lenses or windows used in front of the camera

History and Theory of

Infrared Technology

History and Theory of Infrared Technology

Less than 200 years ago the existence

of the infrared portion of the

electromagnetic spectrum wasn’t even suspected. The original signicance of the infrared spectrum, or simply “the infrared” as it is often called, as a form of heat radiation is perhaps less obvious

today than it was at the time of its

discovery by Sir William Herschel in 1800 (Figure 1).

Figure 1. Sir William Herschel (1738–1822)

The discovery was made accidentally

during the search for a new optical

material. Sir William Herschel – Royal Astronomer to King George III of England, and already famous for his discovery of the planet Uranus – was searching for an optical lter material to reduce the brightness of the sun’s image in telescopes during solar observations.

While testing dierent samples of colored

glass which gave similar reductions in brightness, he was intrigued to nd that some of the samples passed very little of the sun’s heat, while others passed so

much heat that he risked eye damage

after only a few seconds’ observation.

Herschel was soon convinced of the

necessity of setting up a systematic

experiment with the objective of nding a single material that would give the

desired reduction in brightness as well as the maximum reduction in heat. He began the experiment by actually

repeating Newton’s prism experiment,

but looking for the heating eect rather

than the visual distribution of intensity in the spectrum. He rst blackened the bulb of a sensitive mercury-in-glass thermometer with ink, and with this as

his radiation detector he proceeded to

test the heating eect of the various

colors of the spectrum formed on the top of a table by passing sunlight through a

glass prism. Other thermometers, placed outside the sun’s rays, served as controls.

As the blackened thermometer was moved slowly along the colors of the spectrum, the temperature readings

showed a steady increase from the

violet end to the red end. This was not entirely unexpected, since the Italian researcher, Landriani (Figure 2), in a similar experiment in 1777, had observed much the same eect. It was Herschel, however, who was the rst to recognize

that there must be a point where the

heating eect reaches a maximum, and that measurements conned to the visible portion of the spectrum failed to

locate this point.

Figure 2. Marsilio Landriani (1746–1815)

Appendix C

Moving the thermometer into the

dark region beyond the red end of the

spectrum, Herschel conrmed that

the heating continued to increase. The

maximum point, when he found it, lay well beyond the red end – in what is known today as the infrared wavelengths.

When Herschel revealed his discovery,

he referred to this new portion of the electromagnetic spectrum as the thermometrical spectrum. The radiation itself he sometimes referred

to as dark heat, or simply the invisible rays. Ironically, and contrary to popular opinion, it wasn’t Herschel who

originated the term infrared. The word only began to appear in print around

75 years later and it is still unclear who should receive credit as the originator.

Herschel’s use of glass in the prism of his

original experiment led to some early

controversies with his contemporaries

about the actual existence of the infrared

wavelengths. Dierent investigators, in attempting to conrm his work, used various types of glass indiscriminately, having dierent transparencies in the infrared. Through his later experiments,

Herschel was aware of the limited

transparency of glass to the newlydiscovered thermal radiation, and he was

forced to conclude that optics for the infrared would probably be doomed to

the use of reective elements exclusively (plane and curved mirrors). Fortunately, this proved to be true only until 1830, when the Italian investigator Melloni (Figure 3) made his great discovery that naturally occurring rock salt (NaCl) – which was available in large enough

natural crystals to be made into lenses

and prisms – is remarkably transparent

to the infrared. The result was that rock salt became the principal infrared optical material and remained so for the next hundred years until the art of synthetic crystal growing was mastered

in the 1930s.

Figure 3. Macedonio Melloni (1798–1854)

Thermometers, as radiation detectors, remained unchallenged until 1829, the year Nobili invented the thermocouple. (Herschel’s own thermometer could be read to 0.2°C (0.036°F), and later models were able to be read to 0.05°C (0.09°F).)

Then a breakthrough occurred; Melloni connected a number of thermocouples

in series to form the rst thermopile. The new device was at least 40 times as sensitive as the best thermometer of the day for detecting heat radiation – capable

of detecting the heat from a person standing three meters away.

The rst so-called heat-picture became possible in 1840, the result of work by Sir John Herschel, son of the discoverer of

infrared and a famous astronomer in his own right. Based upon the dierential

evaporation of a thin lm of oil when

exposed to a heat pattern focused upon

it, the thermal image could be seen by

reected light where the interference

eects of the oil lm made the image

History and Theory of Infrared Technology

visible to the eye. Sir John also managed to obtain a primitive record of the thermal image on paper, which he called

a thermograph.

Figure 4. Samuel P. Langley (1834–1906)

The improvement of infrared-detector sensitivity progressed slowly. Another major breakthrough, made by Langley (Figure 4) in 1880, was the invention

of the bolometer. This consisted of a thin blackened strip of platinum connected in one arm of a Wheatstone bridge circuit upon which the infrared radiation was focused and to which a

sensitive galvanometer responded. This instrument is said to have been able to

detect the heat from a cow at a distance

of 400 meters.

An English scientist, Sir James Dewar, rst introduced the use of liqueed gases as cooling agents (such as liquid nitrogen with a temperature of –196°C (–320.8°F)) in low temperature research. In 1892 he invented a unique vacuum insulating

container in which it is possible to store

liqueed gases for entire days. The common thermos bottle, used for storing hot and cold drinks, is based upon his invention.

Between the years 1900 and 1920, the inventors of the world “discovered”

infrared. Many patents were issued for

devices to detect personnel, artillery, aircraft, ships – and even icebergs. The rst operating systems, in the modern sense, began to be developed during World War I, when both sides had research programs devoted to the

military exploitation of the infrared. These programs included experimental

systems for enemy intrusion/detection, remote temperature sensing, secure communications, and “ying torpedo” guidance. An infrared search system

tested during this period was able to detect an approaching airplane at a

distance of 1.5 km (0.94 miles) or a person more than 300 meters (984 ft.) away.

The most sensitive systems up to this time were all based upon variations of the bolometer idea, but the period between the two world wars saw the development of two revolutionary new infrared detectors: the image converter and the photon detector. At rst, the image converter received the greatest attention by the military, because it enabled an observer for the rst time in history to literally see in the dark. However, the sensitivity of the image converter was limited to the near infrared wavelengths,

and the most interesting military targets

(enemy soldiers) had to be illuminated by infrared search beams. Since this involved the risk of giving away the observer’s position to a similarly-equipped enemy observer, it is understandable that military interest in the image converter eventually faded.

The tactical military disadvantages of so-called active (search beam

Appendix C

equipped) thermal imaging systems provided impetus following World War II for extensive secret military infrared-

research programs into the possibilities

of developing passive (no search beam) systems around the extremely sensitive photon detector. During this period,

military secrecy regulations completely

prevented disclosure of the status of infrared-imaging technology. This

secrecy only began to be lifted in the

middle of the 1950s and from that time adequate thermal-imaging devices nally began to be available to civilian science

and industry.

Theory of Thermography

Introduction

The subjects of infrared radiation and the related technique of thermography are still new to many who will use an infrared

camera. In this section, the theory behind thermography will be given.

The Electromagnetic Spectrum

The electromagnetic spectrum (Figure

5) is divided arbitrarily into a number of wavelength regions, called bands,

distinguished by the methods used to produce and detect the radiation. There is no fundamental dierence between radiation in the dierent bands of the electromagnetic spectrum. They are all

governed by the same laws and the only

dierences are those due to dierences

in wavelength.

Thermography makes use of the infrared

spectral band. At the short-wavelength

end of the spectrum the boundary lies at

the limit of visual perception, in the deep red. At the long-wavelength end of the spectrum it merges with the microwave radio wavelengths in the millimeter

range.

The infrared band is often further

subdivided into four smaller bands, the

boundaries of which are also arbitrarily

Figure 5. The electromagnetic spectrum

10 nm 100 nm 1 µm 10 µm 100 µm 1 mm 10 mm 100 mm 1 m 10 m 100 m 1 km

1 X-ray 2 UV 3 Visible 4 IR 5 Microwaves 6 Radio waves

2 µm

1234 56

13 µm

History and Theory of Infrared Technology

chosen. They include: the near infrared

(0.75–3 µm), the middle infrared (3–6 µm), the far infrared (6–15 µm), and the extreme infrared (15–100 µm). Although the wavelengths are given in µm (micrometers), other units are often used to measure wavelength in this spectral region, for exmple, nanometer (nm) and Ångström (Å).

The relationships between the dierent

wavelength measurements is:

10,000 Å = 1,000 nm = 1 µm

Blackbody Radiation

A blackbody is dened as an object

that absorbs all radiation that impinges

on it at any wavelength. The apparent

misnomer black relating to an object emitting radiation is explained by

Kirchho’s Law (after Gustav Robert Kirchho, shown in Figure 6), which

states that a body capable of absorbing

all radiation at any wavelength is equally

capable in the emission of radiation.

Figure 6. Gustav Robert Kirchho (1824–1887)

The construction of a blackbody source

is, in principle, very simple. The radiation

characteristics of an aperture in an

isothermal cavity made of an opaque

absorbing material represents almost

exactly the properties of a blackbody. A

practical application of the principle to the construction of a perfect absorber of radiation consists of a box that is light tight except for an aperture in one of

the sides. Any radiation that then enters

the hole is scattered and absorbed

by repeated reections, so only an innitesimal fraction can possibly escape.

The blackness which is obtained at the aperture is nearly equal to a blackbody

and almost perfect for all wavelengths.

By providing such an isothermal cavity with a suitable heater, it becomes what is termed a cavity radiator. An isothermal cavity heated to a uniform temperature generates blackbody radiation, the

characteristics of which are determined

solely by the temperature of the cavity. Such cavity radiators are commonly used

as sources of radiation in temperature reference standards in the laboratory for

calibrating thermographic instruments, such as a FLIR Systems camera, for

example.

If the temperature of blackbody radiation

increases to more than 525°C (977°F), the source begins to be visible so that it no

longer appears black to the eye. This is the incipient red heat temperature of the

radiator, which then becomes orange

or yellow as the temperature increases

further. In fact, the denition of the so-

called color temperature of an object is the temperature to which a blackbody

would have to be heated to have the

same appearance.

Now consider three expressions that

describe the radiation emitted from a blackbody.

Appendix C

Planck’s Law

Figure 7. Max Planck (1858–1947)

Max Planck (Figure 7) was able to describe

the spectral distribution of the radiation from a blackbody by means of the following formula:

πhc3 Wλb =

_______________

× 10–6 [Watt/m µ m ]

(ehc/λkT

– 1)

where:

Wλb Blackbody spectral radiant emittance

at wavelength

λb

c Velocity of light = 3 × 108 m/s h Planck’s constant = 6.6 × 10

–34

Joule

sec

k Boltzmann’s constant = 1.4 × 10

–23

Joule/K

T Absolute temperature (K) of a

blackbody

λ Wavelength (µm)

The factor 10–6 is used since spectral emittance in the curves is expressed in

Watt/mm. If the factor is excluded, the dimension will be Watt/mµm.

Planck’s formula, when plotted graphically for various temperatures, produces a family of curves. Following

any particular Planck curve, the spectral emittance is zero at λ = 0, then increases rapidly to a maximum at a wavelength µmax and after passing it approaches zero again at very long wavelengths. The higher the temperature, the shorter the wavelength at which maximum occurs. See Figure 8.

Wien’s Displacement Law

By dierentiating Planck’s formula with respect to λ and nding the maximum, we have:

2898

max

______

[µm]

This is Wien’s formula (after Wilhelm Wien, shown in Figure 9), which expresses mathematically the common observation that colors vary from red to orange or

yellow as the temperature of a thermal

radiator increases (Figure 10). The wavelength of the color is the same as

0 2 4 6 8 10 12 14

900 K

800 K

700 K

600 K

500 K

Spectral radiant emittance [W/cm

× 10

(µm)]

Wa velength (µm)

Figure 8. Blackbody spectral radiant emittance according to Planck’s law, plotted for various absolute temperatures.

History and Theory of Infrared Technology

the wavelength calculated for λ

max

. A good approximation of the value of µmax for a given blackbody temperature is obtained by applying the rule-of-thumb 3,000/T µm. Thus, a very hot star such as Sirius (11,000 K), emitting bluish-white

light, radiates with the peak of spectral

radiant emittance occurring within

the invisible ultraviolet spectrum at wavelength 0.27 µm.

The sun (approx. 6,000 K) emits yellow light, peaking at about 0.5 µm in the middle of the visible light spectrum.

At room temperature (300 K) the peak of radiant emittance lies at 9.7 µm in the far infrared, while at the temperature of liquid nitrogen (77 K) the maximum of the almost insignicant amount of radiant emittance occurs at 38 µm in the extreme infrared wavelengths.

By integrating Planck’s formula from λ = 0 to λ = ∞, we obtain the total radiant emittance (Wb) of a blackbody:

Wb = σT4 [Watt/m] This is the Stefan-Boltzmann formula

(after Josef Stefan and Ludwig Boltzmann, shown in Figure 11), which states that the total emissive power of a

blackbody is proportional to the fourth power of its absolute temperature.

Graphically, Wb represents the area below the Planck curve for a particular

temperature. It can show that the radiant emittance in the interval λ = 0 to λ

max

only 25% of the total, which represents

Figure 9. Wilhelm Wien (1864–1928)

–1

0 5 10 15 20 25 30

1000 K

900

800

700

600

500

400

300

200

100

Spectral radiant emittance [W/cm

(µm)]

Wa velength (µm)

Figure 10. Planckian curves plotted on semilog scales from 100 K to 1000 K. The dotted line represents the locus of maximum radiant emittance at each temperature as described by Wien’s displacement law.

Figure 11. Josef Stefan (1835–1893) and Ludwig Boltzmann (1844–1906)

Appendix C

approximately the amount of the sun’s radiation that lies inside the visible light

spectrum.

Using the Stefan-Boltzmann formula

to calculate the power radiated by the

human body, at a temperature of 300 K

and an external surface area of approx.  m, we obtain 1 kW. This power loss could not be sustained if it were not for the compensating absorption of radiation from surrounding surfaces at

room temperatures, which do not vary

too drastically from the temperature of

the body – or, of course, the addition of

clothing.

Non-blackbody Emitters

So far, only blackbody radiators and blackbody radiation have been discussed. However, real objects almost never comply with these laws over an extended wavelength region – although they may approach the blackbody behavior in certain spectral intervals. For example, a

certain type of white paint may appear

perfectly white in the visible light spectrum, but becomes distinctly gray at about 2 µm, and beyond 3 µm it is

almost black.

There are three processes that can

prevent a real object from acting like

a blackbody: a fraction of the incident

radiation α may be absorbed, a fraction ρ may be reected, and a fraction τ may be

transmitted. Since all of these factors are

more or less wavelength dependent, the

subscript λ is used to imply the spectral

dependence of their denitions. Thus:

The spectral absorptance α•

= the ratio

of the spectral radiant power absorbed by an object to that incident upon it.

The spectral reectance ρ•

= the ratio

of the spectral radiant power reected by an object to that incident upon it.

The spectral transmittance τ•

= the

ratio of the spectral radiant power transmitted through an object to that incident upon it.

The sum of these three factors must always add up to the whole at any

wavelength, so we have the relation: αλ + ρλ + τλ = 1 For opaque materials τλ = 0 and the

relation simplies to: αλ + ρλ = 1 Another factor, called the emissivity, is

required to describe the fraction ε of the radiant emittance of a blackbody

produced by an object at a specic temperature. Thus, we have the denition: spectral emissivity ελ = the

ratio of the spectral radiant power from an object to that from a blackbody at the

same temperature and wavelength. Expressed mathematically, this can

be written as the ratio of the spectral emittance of the object to that of a blackbody as follows:

Wλ0 ελ =

_____

λb

Generally speaking, there are three types of radiation sources, distinguished by the

ways in which the spectral emittance of

each varies with wavelength (Figures 12 and 13).

A blackbody, for which • ελ = ε = 1 A graybody, for which • ελ = ε = constant

less than 1 A selective radiator, for which • ε varies

with wavelength

History and Theory of Infrared Technology

According to Kirchho’s law, for any material the spectral emissivity and

spectral absorptance of a body are

equal at any specied temperature and wavelength. That is:

ελ = α

From this we obtain, for an opaque material (since αλ + ρλ = 1):

ελ + ρλ = 1

For highly polished materials ελ

approaches zero, so for a perfectly reecting material (for example. a perfect mirror) we have:

ρλ = 1

For a graybody radiator, the StefanBoltzmann formula becomes:

W = εσT4 [Watt/m]

This states that the total emissive power

of a graybody is the same as a blackbody at the same temperature reduced in proportion to the value of ε from the graybody.

Blackbody

Selective radiator

Graybody

Spectral radiant emittance

Wa velength (µm)

Figure 12. Spectral radiant emittance of three types of radiators.

Blackbody

Selective radiator

Graybody

Spectral emissivity

Wa velength

1.0

0.5

0.0

Figure 13. Spectral emissivity of three types of radiators.

Infrared Semi-transparent Materials

Consider a non-metallic, semi-

transparent body in the form of a thick at plate of plastic material. When the

plate is heated, radiation generated within its volume must work its way

toward the surfaces through the material in which it is partially absorbed.

Moreover, when it arrives at the surface,

some of it is reected back into the

interior. The back-reected radiation is again partially absorbed, but some of it arrives at the other surface, through which most of it escapes, but part of it is reected back again. Although the progressive reections become weaker and weaker, they must all be added up

when the total emittance of the plate is sought. When the resulting geometrical

series is summed, the eective emissivity of a semi-transparent plate is obtained as:

(1 –ρλ) (1 – τλ)

ελ =

______________

1 – pλt

When the plate becomes opaque this formula is reduced to the single formula:

ελ = 1 – p

Appendix C

This last relation is a particularly

convenient one, because it is often easier

to measure reectance than to measure

emissivity directly.

The Measurement Formula

As already mentioned, when viewing an object, the camera receives radiation not only from the object itself, it also

collects radiation from the surroundings

reected via the object surface. Both

these radiation contributions become attenuated to some extent by the atmosphere in the measurement path. To this comes a third radiation contribution from the atmosphere itself.

This description of the measurement

situation, as illustrated in Figure 14, is so

far a fairly true description of the real conditions. What has been neglected could for instance be sun light scattering in the atmosphere or stray radiation from

intense radiation sources outside the eld of view. Such disturbances are dicult to quantify. However, in most cases

they are fortunately small enough to be

neglected. In case they are not negligible, the measurement conguration is likely

to be such that the risk for disturbance

is obvious, at least to a trained operator.

It is then his responsibility to modify

the measurement situation to avoid the disturbance (by changing the viewing direction, shielding o intense radiation sources, etc.).

Accepting the description above, we can use Figure 14 to derive a formula for the

calculation of the object temperature from the calibrated camera output.

Assume that the received radiation

power W from a blackbody source of temperature T

source

on short distances

generates a camera output signal U

source

that is proportional to the power input

(power linear camera). We can then write (Equation 1):

source

= CW (T

source

) or, with simplied notation: U

source

= CW

source

where C is a constant.

Figure 14. A schematic representation of the general thermographic measurement situation.

ε τ W

obj

ε W

obj

Surroundings

Object

Atmosphere

Camera

re

= 1

obj

(1 – ε) τ W

re

(1 – ε) W

re

(1 – τ) W

atm

re

History and Theory of Infrared Technology

Should the source be a graybody with

emittance ε, the received radiation would

consequently be εW

source

We are now ready to write the three collected radiation power terms:

Emission from the object1. = ετW

obj

, where

ε is the emittance of the object and τ is

the transmittance of the atmosphere. The object temperature is T

obj

Reected emission from ambient sources2.

= (1 – ε)τW

re

, where (1 – ε) is the

reectance of the object. The ambient

sources have the temperature T

re

It has here been assumed that the temperature T

re

is the same for all

emitting surfaces within the half-

sphere seen from a point on the object surface. This is of course sometimes a

simplication of the true situation. It is, however, a necessary simplication in order to derive a workable formula,

and T

re

can – at least theoretically – be

given a value that represents an ecient

temperature of a complex surrounding.

Note also that we have assumed that the emittance for the surroundings = 1. This is correct in accordance with Kirchho’s law: All radiation impinging on the surrounding surfaces will eventually

be absorbed by the same surfaces.

Thus the emittance = 1. (Note, though,

that the latest discussion requires the complete sphere around the object to be

considered.)

Emission from the atmosphere3. = (1 – τ)τW

atm

, where (1 – τ) is the

emittance of the atmosphere. The temperature of the atmosphere is T

atm

The total received radiation power can now be written (Equation 2):

tot

= ετW

obj

+ (1 – ε)τW

re

+ (1 – τ)W

atm

We multiply each term by the constant

C of Equation 1 and replace the CW products by the corresponding U according to the same equation, and get (Equation 3):

tot

= ετU

obj

+ (1 – ε)τU

re

+ (1 – τ)U

atm

Solve Equation 3 for U

obj

(Equation 4):

1 1 – ε 1 – τ U

obj

___

tot

–

_____

re

–

_____

atm

ετ ε ετ

This is the general measurement formula used in all the FLIR Systems

thermographic equipment. The voltages of the formula are given in Table 1.

Table 1. Voltages

obj

Calculated camera output voltage for a

blackbody of temperature T

obj

, which is a

voltage that can be directly converted into

true requested object temperature.

tot

Measured camera output voltage for the

actual case.

re

Theoretical camera output voltage for a

blackbody of temperature T

re

according to

the calibration.

atm

Theoretical camera output voltage for a

blackbody of temperature T

atm

according to

the calibration.

The operator has to supply a number of

parameter values for the calculation:

object emittance ε• relative humidity•

T•

atm

object distance (D•

obj

)

(eective) temperature of the object •

surroundings or the reected ambient temperature T

re

the temperature of the atmosphere • T

atm

Appendix C

This task can sometimes be a heavy

burden for the operator since there are

normally no easy ways to nd accurate values of emittance and atmospheric

transmittance for the actual case. The two temperatures are normally less of a

problem provided the surroundings do

not contain large and intense radiation sources.

A natural question in this connection is:

How important is it to know the right

values of these parameters? It could

be of interest to get a feeling for this problem by looking into some dierent measurement cases and compare the

relative magnitudes of the three radiation terms. This will give indications about when it is important to use correct values

for which parameters.

Figures 15 and 16 illustrate the relative

magnitudes of the three radiation contributions for three dierent object

temperatures, two emittances, and two

spectral ranges: SW and LW. Remaining

parameters have the following xed values:

τ = 0.88•

T•

re

= +20°C (+68°F)

T•

atm

= +20°C (+68°F)

It is obvious that measurement of low

object temperatures is more critical than measuring high temperatures since the “disturbing” radiation sources are

relatively much stronger in the rst case. Should the object emittance be low, the situation would be more dicult.

We have nally to answer a question

about the importance of being allowed

to use the calibration curve above the highest calibration point, what we call

extrapolation. Imagine that we in a

certain case measure U

tot

= 4.5 volts. The

highest calibration point for the camera

was in the order of 4.1 volts, a value unknown to the operator. Thus, even if

the object happened to be a blackbody

obj

= U

tot

), we are actually performing

extrapolation of the calibration

curve when converting 4.5 volts into

temperature.

0°C (3 2°F)

0.6

0.8

20 °C ( 68°F)

Object T e mperat ure

Emittance

50 °C ( 1 22 °F)

Object Radia tion

Reect ed Radia tion

At mosp here Rad iation

Figure 15. Relative magnitudes of radiation sources under varying measurement conditions (SW camera). Fixed parameters: τ = 0.88; T

re

20°C (+68°F); T

atm

= 20°C (+68°F).

0°C (3 2°F)

0.6

0.8

20 °C ( 68°F)

Object T e mperat ure

Emittance

50 °C ( 1 22 °F)

Object Radia tion

Reect ed Radia tion

At mosp here Rad iation

Figure 16. Relative magnitudes of radiation sources under varying measurement conditions (LW camera). Fixed parameters: τ = 0.88; T

re

20°C (+68°F); T

atm

= 20°C (+68°F).

History and Theory of Infrared Technology

Let us now assume that the object is

not black, it has an emittance of 0.75 and the transmittance is 0.92. We also

assume that the two second terms of

Equation 4 amount to 0.5 volts together. Computation of U

obj

by means of

Equation 4 then results in U

obj

= 4.5 /

0.75 / 0.92 – 0.5 = 6.0. This is a rather extreme extrapolation, particularly when considering that the video amplier might limit the output to 5 volts. Note, though, that the application

of the calibration curve is a theoretical

procedure where no electronic or other limitations exist. We trust that if there had been no signal limitations in the

camera, and if it had been calibrated far beyond 5 volts, the resulting curve would have been very much the same as our real curve extrapolated beyond 4.1 volts, provided the calibration algorithm is based on radiation physics, like the FLIR Systems algorithm. Of course there must

be a limit to such extrapolations.

Appendix D

Command Syntax

Examples for A320 Resource Socket Services

Physical Interface

From the camera, there is only one physical interface for data transfer, Ethernet. Analog video also exists, but

is not considered a data transfer interface.

Low Level Protocols

For Ethernet, only TCP/IP is supported.

The camera should seamlessly work on

any LAN, provided that a proper IP adress, netmask, and possibly gateway is set in

the camera.

No FLIR specic device drivers are required, so any type of computer and

operating systems supporting TCP/IP should work.

Functionality

The two main ways of controlling the camera are through the command control interface and through the resource control interface. Image

streaming, le transfer, and other functionality is provided through the IP services interface.

Command Control

Commands can be given to the

“commandshell” in the camera. Some commands are “standardcommands” like “dir” and “cd” that operate directly

on the camera themselves. Others rely on camera resources (see below), for example the “level” command.

It is possible to run independent instances of the command shell with the

“telnet” service on established TCP/IP

connections.

Resource Control

Most, but not all, software functionality

is exposed through software resources. Those that are familiar with the Microsoft

Windows registry will recognize this concept. However, in the camera a

resource node can also represent a

software function that, upon read or write, actively interacts with the

camera software.

The following lists some introductory details about software resources and resource nodes.

Resources are organized in a hierarchy • (like in a tree).

Resource nodes can be data holders (of • for example, calibration data).

Resource nodes can be connected • to hardware (for example, to internal temperature sensor values).

Resource nodes can be connected to • software (for example, to spotmeter values)

Resource nodes have a type (double, • int32, ascii, etc.) and certain attributes (readonly, read/write).

Resources can be reached through • commands like “rls” and “rset,” and

through the IP FLIR Resource socket

service.

IP Services

It is possible to access the system independent of physical interface using

TCP/IP with exposed services such as telnet, ftp, http, CIFS, FLIR resource socket, and FLIR RTP. More than one service and possibly more than one

Command Syntax Examples for

A320 Resource Socket Services

instance of the service can be run

simultaneously.

telnet

Command control, mainly for manual

typing. Typical clients are the standard telnet command on a PC or teraterm.

FTP

File transfer to/from the camera using FTP client software on a PC. Typical clients are the standard FTP command on a PC or WS_FTP.

http

Web server. Typical clients are Microsoft

Internet Explorer and Firefox.

CIFS

PC network le access service. This service makes it possible to map a drive on the PC to the camera le system. The

intended client software is built into all

relevant Windows versions. By default,

only the image folders are accessible in

this way. To access the whole ash le system, mount the xxx.xxx.xxx.xxx/root$ drive.

FLIR Resource Socket

It is possible to directly read and write nodes of the software resource tree from

a PC. Standard sockets are used, but there are no standard client programs available.

Image Streaming

Set-up

The available streams are described and presented using SDP (Session Description Language, RFC 2327). The SDP content is accessed using RTSP (Real Time Streaming Protocol, RFC 2326).

The RTSP/DESCRIBE command lists the

available streams.

Table 1

MPEG4 Compressed video in three sizes

(640x480, 320x240, 160x128) FCAM FLIR usage only. Raw IR-signal or temperature linear IR-image

(two types) in two sizes (320×240,

160×120). The pixels are transferred in

network byte order (big endian).

The RTSP/SETUP command establishes an RTP-based transport session using one of

the formats.

The RTSP/GET_PARAMETER command

gets the current framerate and format.

The RTSP/SET_PARAMETER command

sets the current framerate and format.

The RTSP/PLAY and RTSP/PAUSE

commands control the image stream.

The RTSP/TEARDOWN command closes

the transport session.

MPEG4

The MPEG4 streams use RTP/UDP/IP for transport (RTP = Real time Transport Protocol, RFC 1889). The MPEG bit stream is packetized according to RFC 3016.

On the receiver side, FLIR supplies a DirectShow component (Win32, PC platform) which is able to receive the MPEG4 bit stream. The component is able to receive the MPEG4 bit stream according to RFC 3016. The bit stream is reassembled and forwarded as video samples of FOURCC type MP4V. Several MPEG4 decoders can be used, for example from 3ivx ($7 per license) or a free decoder (dshow). The DirectShow

component can be used by any application that wishes to display the

MPEG4 video stream.

Appendix D

IR Streams

The raw IR streams use RTP/UDP/IP

for transport. The transport format is

according to RFC 4175 (RTP Payload Format for Uncompressed Video).

The raw image frame rates are up to

about 7.8 Hz.

These raw formats are provided:

Table 2.

0 16-bit uncompressed IR image linear in signal 1 16-bit uncompressed IR image linear in

temperature, resolution 0.1 K (range 0–6553 K)

 16-bit uncompressed IR image linear in

temperature, resolution 0.01 K (range 0–655 K)

On the client side, FLIR supplies a DirectShow component (Win32, PC platform) which is able to setup and receive the IR streams. The IR stream is

reassembled and forwarded as samples

of FOURCC type Y160 (this FOURCC type is only preliminary at this stage). The

DirectShow component can be used by any application that wishes to display the IR stream or want to grab samples from the stream.

DHCP

The camera supports the client part of

the Dynamic Host Conguration Protocol (DHCP).

Remote Detection

Multicast DNS (Bonjour)

To query Bonjour for local FLIR IR

cameras, use:

Service name: ir-ircam•

Protocol type: _tcp• Domain: local•

Table 3: TXT records

Name Value Explanation

Model S ID A320 Camera ID

GID Gen_A Generic ID

SI FFF_RTSP Streaming Interface SIV 1.0.0 Streaming interface

version

CI RTREE Command interface CIV 1.0.0 Command interface

version

For more information, see

www.dns-sd.org.

For complete information, see FLIR’s A320

ICD manual.

Quick Summary of FLIR IR Cameras

Photon A320 A325

Sensor Type bolometer bolometer bolometer Pixel Resolution 324×256 320×240 320×240 Pixel Pitch 38μm m m Spectral Ranges 7.5μm – 13.5μm 7.5μm – 13.0μm 7.5μm – 13.0μm

Dynamic Range 14-bit

14-bit, Signal +

Temp Linear

14-bit, GiGE Vision +

Gen<i>Cam

Internal Temperature Calibration

• •

Ambient Drift Compensation • • • Temperature Calibration Imager Only • • Full Frame Rate 30 fps 30 fps 60 fps

Digital Data Output

GigE (optional)

or Serial

Ethernet GigE

Analog Video RS-170 RS-170

Command and Control

RS-232 or

GigE (optional)

Ethernet GigE

On-Board Analytics • Motorized Focus • • Auto Focus • • Digital I/O • • Triggering Options • • •

Remote Camera Control Proprietary

Web, TCP/IP

(Open Protocol)

GeniCam

PoE Compatible • Messaging FTP, SMTP SDK Support • • • LabVIEW Compatibility • • •

Aperture

f/1.3, f/1.4, f/1.4, f/1.7

lens dependent

f/1.3 f/1.3

Filtering Options

Available Optics

14.25mm 19mm 35mm 50mm

18mm 30mm 10mm

Appendix E

Published by FLIR Systems Incorporated www.goinfrared.com

FLIR Photon, A320, A325 Manual Book

Specifications and Main Features

Frequently Asked Questions

User Manual