848T High Density
Temperature
Measurement Validation
Diagnostic
Why On-Line Validation?
“Occasionally, a thermocouple may fail in a manner that gives randomly
high temperature readings.”
“..the temperature alarm and shutdown systems should reliably
distinguish between these false readings and an actual runaway.
Otherwise needless risk and expense are introduced by unnecessary
reactor shutdowns.”
“Perhaps even more important is the loss of confidence in the shutdown
systems by operations personnel – the shutdown interlock might be
bypassed at a time it is most needed”
From an article by Arthur Smart, Jr., Celanese Chemical Co. (1981)
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Measurement Validation:
What is it?

Industry First Diagnostic

Provides online validation of data during
measurement

Approximates standard deviation of the input
error in relation to a measurement trend

Allows the user to understand abnormal
deviations in the measurement

Also calculates PV rate of change – to detect
abnormally fast process changes
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Provided in 848T Fieldbus
Measurement Validation:
Why use it?

Measurement qualification before control
– Only use valid measurements in the process control strategy

Allows for validation of data to detect degraded conditions
– Provides alerts to the user of the abnormal temperature behavior
– Online Sensor failure can be detected before the sensor actually
fails. Proactively change-out sensors before failure. Avoid
Shutdowns

Know process rate of change
– Detect abnormally fast process increases or decreases, and be
alerted before they become unsafe
– Alerts allow unsafe condition detection BEFORE upper or lower
limits are reached
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Measurement Validation Diagnostic
-Industry First on the 848T Fieldbus
Industry first diagnostic provides validation of measurement data which can help
identify the following issues:
– On-scale Errors- when the output of a transmitter is within process alarm limits yet does
not accurately indicate actual process information.
– Invalid measurement readings that lay outside of alarm limits when actual process
variable is within acceptable range
– Abnormally fast process rates of change.

Issues are usually due to failing sensors (most common), electronic interference,
corroded termination points, loose electrical connections and runaway reactions
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Prevent Unnecessary Shutdowns,
Optimize Processes And Increase Safety

Avoid unnecessary process shutdowns
– Detect measurement abnormalities and take preventative action before shutdown is
necessary
•


Avoid loss of revenue and safety concerns associated with shutdown process
– Determine validity of measurement points that are outside of alarm limits
Avoid on-scale failure related issues
– Identify on-scale failures and take action before process efficiency and safety is
compromised
•
Improve Energy Efficiency
•
Increase Product Quality
•
Improve Product Yield and Throughput
– On-scale failure can lead to process shutdown
Avoid unsafe process conditions
– Detect abnormally fast process rates of change
before alarm state is reached
– The earlier unsafe conditions are identified the
more manageable they are
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A Closer Look: How Measurement
Validation Works


By evaluating sample to sample deviations over an averaged trend, Measurement
Validation can detect abnormal deviations in measurements which in turn can be related to
a specific issue
Sample to sample deviation is calculated as follows:
–
–
–
–
•
A rolling average of raw process measurements is taken to create a trend line
the difference of the most current measurement to the average is calculated, generating a pseudo rate of
change value
This value is then compared to a rolling average of pseudo rate of change values- the difference of the two
values resulting in the deviation output which is then compared to customer selected alert limit
The deviation output is then compared a customer selected limit which, depending on severity setting, will
generate a status that the user can use to take action.
Process Rate of Change is calculated by
measuring the time and difference between
samples and applying a moving filter to
eliminate high frequency noise.
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Case Study



Amoco made these calculations for a particular
Hydrocracker unit
During study, 78 “incidents” occurred
The measurement validation analysis helped the
operators differentiate between
– 12 valid high temperature excursions and 66 malfunctions
• One malfunction due to multiplexor failure
• 2 were due to radio interference
• 63 were due to the failure of 63 Thermocouples.
– Operators were able to perform preventative maintenance
instead of process shutdowns
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Measurement Validation:
Configuration

Alert Limits
– Deviation and Rate of change Increasing and
Decreasing limits

Alert Severity
– Failure, warning, advisory, or not used

Deadband
– Decide how sensitive your alerts are, i.e. how much
they have to go down before the alarm is surpressed.
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27-Jun-01, Slide 9
Measurement Validation:
Status Handling

Advisory Plant Web Alerts available
– Excessive Deviation and Excessive Rate of Change

Four levels of validation severity for both Rate of
Change and Deviation
– Failure – Sets PV to fail status, triggers Failed plant
web alert (sensor failure)
– Warning – Sets PV to uncertain status, triggers
Maintenance plant web alert (sensor degraded)
– Advise – No change to PV status, triggers Advisory
plant web alert
– Not Used – Provides Rate of Change or Deviation
output with no status handling
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27-Jun-01, Slide 10
Measurement Validation:
Dashboard DD
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27-Jun-01, Slide 11
848T New Model Option Codes
New Option Codes
D04
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848T
Measurement Validation Diagnostic
Key Takeaways

High Density transmitters provide a cost effective
method of measuring moderate to large numbers
of measurement points

New input options allow the 848T to be used in
more applications

Appropriate installation practices allow the 848T
to be used in critical applications

Measurement validation provides a new level of
integrity for a highly reliable measurement.
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