Rosemount Manual: Rosemount 3051S Series of Instrumentation – Section 6: Advanced Pressure Diagnostics for Foundation Fieldbus Manuals & Guides

Reference Manual

00809-0200-4801, Rev CA
July 2010

Rosemount 3051S

Section 6 Advanced Pressure Diagnostics

for F

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . page 6-1

Statistical Process Monitoring Technology . . . . . . . . . . . page 6-2

SPM Configuration and Operation . . . . . . . . . . . . . . . . . . page 6-6

Plugged Impulse Line Detection Technology . . . . . . . . . page 6-17

Configuration of Plugged Impulse Line Detection . . . . . page 6-25

OUNDATION fieldbus

OVERVIEW The 3051S FOUNDATION fieldbus Pressure Transmitter with Advanced

Diagnostics Suite is an extension of the Rosemount 3051S Scalable Pressure
transmitter and takes full advantage of the architecture. The 3051S
SuperModule™ Platform generates the pres sur e mea su rem en t. The
Foundation fieldbus Feature Board is mounted in the PlantWeb housing and
plugs into the top of the SuperModule. The Advanced Diagnostics Suite is a
licensable option on the Foundation fieldbus feature board, and designated by
the option code “D01” in the model number.

The Advanced Diagnostics Suite has two distinct diagnostic functions,
Statistical Process Monitoring (SPM) and Plugged Impulse Line Detection
(PIL), which can be used separately or in conjunction with each other to
detect and alert users to conditions that were previously undetectable, or
provide powerful troubleshooting tools. Figure 6-1 illustrates an overview of
these two functions within the Fieldbus Advanced Diagnostics Transducer
Block.

Figure 6-1. Advanced
Diagnostics Transducer Block
Overview

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Rosemount 3051S

Reference Manual

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July 2010

Statistical Process Monit oring (SPM)

The Advanced Diagnostics Suite features SPM technology to detect changes
in the process, process equipment or installation co nditions of the tr ansmitter.
This is done by modeling the process noise signature (using the statistical
values of mean and standard deviation) under normal conditions and then
comparing the baseline values to current values over time. If a significant
change in the current values is detected, the transmitter can generate an
alert. The SPM can perform its statistical processing on either the primary
value of the field device (e.g. pressure measurement) or any other process
variable available in one of the device’s other Fieldbus function blocks (e.g.
the device sensor temperature, control signal, valve position, or measurement
from another device on the same fieldbus segment). SPM has the capability
of modeling the noise signatures for up to four process variables
simultaneously (SPM1-SPM4). When SPM detects a change in the process
statistical characteristics, it generates an alert. The statistical values are also
available as secondary variables from the transmitter via AI or MAI Function
Blocks if a user is interested in their own analysis or generating their own
alarms.

Plugged Impulse Line (PIL) Diagnostics

STATISTICAL PROCESS
MONITORING
TECHNOLOGY

The Advanced Diagnostics Suite also implements a plugged impulse line
detection algorithm. PIL leverages SPM technolo gy and adds some additional
features that apply SPM to directly detect plugging in pressure measurement
impulse lines. In addition to detecting a change in the process noise
signature, the PIL also provides the ability to automatically relearn new
baseline values if the process condition changes. When PIL detects a plug, a
“Plugged Impulse Line Detected” PlantWeb alert is generated. Optionally, the
user can configure the PIL to, when a plugged impulse line is detected,
change the pressure measurement status quality to “Uncertain”, to alert an
operator that the pressure reading may not be reliable.

IMPORTANT

Running the Advanced Diagnostics Block could affect other block execution
times. We recommend the device be configured as a basic device versus a
Link Master device if this is a concern.

Emerson has developed a unique technolog y, Statistical Process Monitoring,
which provides a means for early detection of abnormal situations in a
process environment. The technology is based on the premise that virtually all
dynamic processes have a unique noise or variation signature when
operating normally. Changes in these signatures may signal that a significant
change will occur or has occurred in the process, process equipment, or
transmitter installation. For example, the noise source may be equipment in
the process such as a pump or agitator, the natural variation in the DP value
caused by turbulent flow, or a combination of both.

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Figure 6-2. Changes in process
noise or variability and affect on
statistical parameters

Rosemount 3051S

The sensing of the unique signature begins with the combination of a high
speed sensing device, such as the Rosemount 3051S Pressure Transmitter,
with software resident in a F
statistical parameters that characterize and quantify the noise or variation.
These statistical parameters are the mean and stand ard deviation of the inp ut
pressure. Filtering capability is provided to separate slow changes in the
process due to setpoint changes from the process noise or variation of
interest. Figure 6-2 shows an example of how the standard deviation value ()
is affected by changes in noise level while the mean or average value ()
remains constant. The calculation of the statistical parameters within the
device is accomplished on a parallel software path to the path used to filter
and compute the primary output signal (e.g. the pressure measurement used
for control and operations). The primary output is not affected in any wa y by
this additional capability.

OUNDATION fieldbus Feature Board to compute

The device can provide the statistical information to the user in two ways.
First, the statistical parameters can be made available to the host system
directly via Foundation fieldbus communication protocol or FF to other
protocol converters. Once available, the system may make use of these
statistical parameters to indicate or detect a change in proce ss conditions. In
the simplest example, the statistical values may be stored in the DCS
historian. If a process upset or equipment problem occurs, these values can
be examined to determine if changes in the values foreshadowed or indicated
the process upset. The statistical values can then be made available to the
operator directly, or made available to alarm or alert software.

Second, the device has internal software that can be used to baseline the
process noise or signature via a learning process. Once the learning process
is completed, the device itself can detect significant changes in the noise or
variation, and communicate an alarm via PlantWeb alert. Typical applications
are change in fluid composition or equipment related problems.

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SPM Functionality A block diagram of the Statistical Process Monitoring (SPM) diagnostic is

shown in Figure 6-3. Note from Figure 6-1 that the 3051S FF has four
Statistical Process Monitoring blocks (SPM1-SPM4). Figure 6-3 illustrates just
one of the SPM blocks. The process variable (which could be either the
measured pressure or some other vari able from the fieldbus segment) is input
to a Statistical Calculations Module where basic high pass filtering is
performed on the pressure signal. The mean (or ave rage) is calculated on the
unfiltered pressure signal, the standard deviation calculated from the filtered
pressure signal. These statistical values are available via handheld
communication devices like the 375 Field Communicator or asset
management software like Emerson Process Management’s AMS
Manager, or distributed control systems with Foundation fieldbus, such as
DeltaV.

Figure 6-3. Diagram of 3051S
FF Statistical Process
Monitoring

™

Device

SPM also contains a learning module that establishes the baseline values for
the process. Baseline values are established under user control at conditions
considered normal for the process and installation . These baseline values are
made available to a decision module that compar es the baseline values to the
most current values of the mean and standard deviation. Based on sensitivity
settings and actions selected by the user via the control input, the diagnostic
generates a device alert when a significant change is detected in either mean
or standard deviation.

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Figure 6-4. Flow Chart of 3051S
FF Statistical Process
Monitoring

Rosemount 3051S

Further detail of the operation of the SPM diagnostic is shown in the
Figure 6-4 flowchart. This is a simplified version showing operation using the
default values. After configuration, SPM calculates mean and standard
deviation, used in both the learning and the monitor ing modes. Once enabled,
SPM enters the learning/verification mode. The baseline mean and standard
deviation are calculated over a period of time controlled by the user (SPM
Monitoring Cycle; default is 15 minutes). The status will be “Learning”. A
second set of values is calculated and compared to the original set to verify
that the measured process is stable and repeatable. During this period, the
status will change to “Verifying”. If the process is st able, the diagnostic will use
the last set of values as baseline values and move to “Monitoring” status. If
the process is unstable, the diagnostic will continue to verify until stability is
achieved.

In the “Monitoring” mode, new mean and standard deviation values are
continuously calculated, with new values available every few seconds. The
mean value is compared to the baseline mean value, and the standard
deviation is compared to the baseline standard deviation value. If either the
mean or the standard deviation has changed more than user-defined
sensitivity settings, an alert is generated via F
may indicate a change in the process, equipment, or transmitter installation.

OUNDATION fieldbus. The alert

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July 2010

NOTE:

The Statistical Process Monitoring diagnostic capability in the Rosemount
3051S F
significant changes in statistical parameters derived from the input process
variable. These statistical parameters relate to the variability of and the noise
signals present in the process variable. It is difficult to predict specifically
which noise sources may be present in a given measurement or control
application, the specific influence of those noise sources on the statistical
parameters, and the expected changes in the noise source s at any tim e.
Therefore, Rosemount cannot absolutely warrant or guarantee that Statistical
Process Monitoring will accurately detect each specific condition under all
circumstances.

OUNDATION fieldbus Pressure Transmitte r calculates and detects

SPM CONFIGURATION
AND OPERATION

SPM Configuration for
Monitoring Pressure

The following section describes the process of configuring and using the
Statistical Process Monitoring diagnostic.

Most Advanced Diagnostics Applications require using the device’s pressure
measurement as the SPM input. To configure the first SPM Block (SPM1) to
monitor the pressure set the following parameters:

SPM1_Block_Tag = TRANSDUCER

NOTE:

By default, as shipped from the factory, the tag of the sensor transducer block
is “TRANSDUCER”. DeltaV does not change the transducer block tags when
the device is installed and commissioned. However, it is possible that other
Fieldbus host systems may change the transducer block tags. If this h appens,
SPM#_Block_Tag must be set to whatever tag was assigned by the host.

SPM1_Block_Type = TRANSDUCER Block
SPM1_Parameter_Index = Pressure (inH
SPM1_User_Command = Learn
(optional) SPM_Monitoring_Cycle = [1 – 5] minutes (see “Other SPM

Settings” on page 6- 7)
(optional) SPM_Bypass_Verification = [Yes/No] (see page 6-7)
Apply all of these above changes to the device. Finally, set

O @ 68 °F)

2

6-6

SPM_Active = Enabled with 1st-order HP Filter
After SPM is enabled, it will spend the first 5 (whatever the

SPM_Monitoring_Cycle is set to) minutes in the learning phase, and then
another 5 minutes in the verification phase. If a steady pro cess is detected at
the end of the verification phase, the SPM will move into the monitoring
phase. After 5 minutes in the monitoring phase, SPM will have the current
statistical values (e.g. current mean and standard deviation), and will begin
comparing them against the baseline values to determine if an SPM Alert is
detected.

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Rosemount 3051S

SPM Configuration for
Monitoring Other
Process Variables

Advanced users may wish use SPM to monitor other Fieldbus parameters
available within the pressure transmitter . Exam ples of such p arameters would
include module sensor temperature, PID control output, valve position, or a
process measurement from another device on the same Fieldbus segment.
Configuration of SPM for other process variables is similar to what is done for
pressure, except that the Block Tag, Block Type, and Parameter Index
parameters are different.

Note that # should be replaced by the num ber of the SPM block which yo u are
configuring (1, 2, 3, or 4).

SPM#_Block_Tag

The tag of the Fieldbus transducer or function block that contains the
parameter to be monitored. Note that the tag must be entered manually –
there is no pull-down menu to select the tag. SPM can also monitor “out”
parameters from other devices. To do this, link the “out” parameter to an input
parameter of a function block that resides in the device, and se t up SPM to
monitor the input parameter.

SPM#_Block_Type

The type of block which was entered into SPM#_Block_Tag. This could be
either a Transducer block, or one of the function blocks.

SPM#_Parameter_Index

The parameter (e.g. OUT, PV, FIELD_VAL) of the transducer or function block
which you want to monitor.

See “Example Configuration of SPM using Function Block” on page 6-12 for
an example of this using DeltaV.

Other SPM Settings Additional information on other SPM settings is shown below:

SPM_Bypass_Verification

If this is set to “Y es”, SPM will skip the verification process, and the first mean
and standard deviation from the learning phase will be taken as the baseline
mean and standard deviation. By skippi ng the verification, the SPM can move
into the monitoring phase more quickly. This parameter should only be set to
“Yes” if you are certain that the process is at a steady-state at the time you
start the Learning. The default (and recommended) setting is “No”.

SPM_Monitoring_Cycle

This is the length of the sample window over which mean and standard
deviation are computed. A shorter sample window means that the statistical
values will respond faster when there are process changes, but there is also a
greater chance of generating false detections. A longer sample window
means that mean and standard deviation will take longer to respond when
there is a process change. The default value is 15 minutes. For most
applications a Monitoring Cycle ranging from 1 to 10 minutes is appropriate.
The allowable range is 1 to 1440 minutes (for software revisions 2.0.x or
earlier, the minimum SPM Monitoring Cycle is 5 minutes).

Figure 6-5 illustrates the effect of the SPM Monitoring Cycle on the Statistical
Calculations. Notice how with a shorter sampling window there is more
variation (e.g. the plot looks noisier) in the trend. With the longer sampling
window the trend looks smoother because the SPM uses process data
averaged over a longer period of time.

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Rosemount 3051S

Figure 6-5. Effect of the SPM
Monitoring Cycle on the
Statistical Values

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SPM#_User_Command

Select “Learn” after all the parameters have been configured to beg in the
Learning Phase. The monitoring phase will start automatically after the
learning process is complete. Select “Quit” to stop th e SPM . “Det ec t” m ay be
selected to return to the monitoring phase.

SPM_Active

The SPM_Active parameter starts the Statistical Process Monitoring when
“Enabled”. “Disabled” (default) turns the diagnostic monitoring off. Must be set
to “Disabled” for configuration. Only set to “Enabled” after fully co nfiguring the
SPM. When Enabling SPM, you may select one of two options:

Enabled with 1st-Order HP Filter

Applies a high-pass filter to the pre ssu re me asur eme nt prio r to calcu lating
standard deviation. This removes the effect of slow or gradual process
changes from the standard deviation calcul ation while preserving the
higher-frequency process fluctuations. Using the high-pass filter reduces
the likelihood of generating a false detection if there is a normal process or
setpoint change. For most diagnostics applications, you will want to use
the filter.

Enabled w/o Filter

This enables SPM without applying the high-pass filter. Without the filter,
changes in the mean of the process variable will cause an increase in the
standard deviation. Use this option only if there are very slow process
changes (e.g. an oscillation with a long period), which you wish to monitor
using the standard deviation.

Configuration of Alerts In order to have SPM generate a PlantWeb alert, the alert limits must be

configured on the mean and/or standard deviation. The three alert limits
available are:

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SPM#_Mean_Lim

Upper and lower limits for detecting a Mean Change

SPM#_High_Variation_Lim

Upper limit on standard deviation for detecting a High Variation condition

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SPM#_Low_Dynamics_Lim

Lower limit on standard deviation for detecting a Low Dynamics condition
(must be specified as a negative number)

All of these limits are specified as a percent change in the statistical value
from its baseline. If a limit is set to 0 (the default setting) then the
corresponding diagnostic is disabled. Fo r ex am p le, if
SPM#_High_Variation_Limit is 0, then SPM# does not detect an increase in
standard deviation.

Figure 6-6 illustrates an example of the standard deviation, with its baseline
value and alert limits. During the monitoring phase, the SPM will continuously
evaluate the standard deviation and compare it against the baseline value. An
alert will be detected if the standard deviation either goes above the upper
alert limit, or below the lower alert limit.

In general, a higher value in any of these limits leads to the SPM diagnostic
being less sensitive, because a greater change in m ean or st andard d eviation
is needed to exceed the limit. A lower value makes the diagnostic more
sensitive, and could potentially lead to false detections.

Figure 6-6. Example Alerts for
Standard Deviation

SPM Operations During operation, the following values are updated for each SPM Block (e.g.

SPM1-SPM4)

SPM#_Baseline_Mean

Baseline Mean (calculated average) of the process variable, determined
during the Learning/Verification process, and representing the normal
operating condition

SPM#_Mean

Current Mean of the process variable

SPM#_Mean_Change

Percent change between the Baseline Mean and the Current Mean

SPM#_Baseline_StDev

Baseline Standard Deviation of the process variable, determined during the
Learning/Verification process, and representing the normal operating
condition

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SPM#_StDev

Current Standard Deviation of the process variable

SPM#_StDev_Change

Percent change between the Baseline Standard Deviation and the Current
Standard Deviation

SPM#_Timestamp

Timestamp of the last values and status for the SPM

SPM#_Status

Current state of the SPM Block. Possible values for SPM Status are as
follows:

Status Value Description

Inactive User Command in “Idle”, SPM not Enabled, or the function block is

not scheduled.

Learning Learning has been set in the User Command, and the initial

baseline values are being calculated

Verifying Current baseline values and previous baseline values or being

compared to verify the process is stable.
Monitoring Monitoring the process and no detections are currently active.
Mean Change Detected Alert resulting from the Mean Change exceeding the Threshold

Mean Limit. Can be caused by a set point change, a load change

in the flow, or an obstruction or the removal of an obstruction in the

process.
High Variation Detected Alert resulting from the Stdev Change exceeding the Threshold

High Variation value. This is an indicator of increased dynamics in

the process, and could be caused by increased liquid or gas in the

flow, control or rotational problems, or unstable pressure

fluctuations.
Low Dynamics Detected Alert resulting from the Stdev Change exceeding the Threshold

Low Dynamics value. This is an indicator for a lower flow, or other

change resulting in less turbulence in the flow.
Not Licensed SPM is not currently purchased in this device.

In most cases, only one of the above SPM status bits will be active at one
time. However, it is possible for “Mean Change Detected” to be active at the
same time as either “High Variation Detected” or “Low Dynamics Detected” is
active.

PlantWeb Alert When any of the SPM detections (Mean Change, High Variation, or Low

Dynamics) is active, a Fieldbus PlantWeb alert in the device “Process
Anomaly Detected (SPM)” will be generated and sent to the host system.
However, note that there is just one SPM PlantWeb alert, and it applies to all
the detections on all four SPM blocks.

Trending Statistical
V a lues in Control System

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SPM Mean and St andard Deviation values may be viewed and/or trended in a
Fieldbus host system through the AI or MAI function blocks.

An Analog Input (AI) block may be used to read either the mean or the
standard deviation from any one of the SPM Blocks. To use the AI block to
trend SPM data, set the CHANNEL parameter to one of the following values: