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Detection and Diagnosis of Plant-wide Oscillations using the Spectral Envelope Method Hailei Jiang , M.A.A. Shoukat Choudhury, and Sirish L. Shah Computer and Process Control group Department of Chemical and Materials Engineering University

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Detection and Diagnosis of Plant-wide Oscillations using the Spectral Envelope Method

Hailei Jiang , M.A.A. Shoukat Choudhury, and Sirish L. Shah Computer and Process Control group Department of Chemical and Materials Engineering University of Alberta

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Outline

Motivation Spectral Envelop Method and its use in

Oscillation Detection & Variable Categorization

Application to industrial data Conclusion Acknowledgement

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Motivation

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Motivation

Oscillations are common in many processes,

whose effects propagate to many units and thus may impact the overall process performance.

For reasons of Safety and Profitability, it is

important to detect and diagnose the plant- wide oscillation.

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Industrial Example

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Objective

Detect oscillations in the process variables. Categorize the variables that have similar

  • scillations.

Deliver useful information on the potential

root-cause(s)

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Spectral Envelop Method and its use in Oscillation Detection & Variable Categorization

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Preliminaries

Assume is an dimensional,

vector-valued time series, for .

Denote as a linear combination of the variables of

:

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Preliminaries (con’t)

Denote the covariance of as: The variance of can be expressed as:

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What is a Spectral Envelope?

With the constraint that

, then is unit-variance.

Power spectra Spectral Envelope ) , ( β ω

g

P

ω π

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Definition of Spectral Envelop

The Spectral Envelope of is defined to be

) (t X

The quantity represents the largest power

(variance) that can be obtained at frequency for any unit-variance scaled series, which is a linear combination of the variables of .

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Simulation example

Noise 0.1 & 0.3 Hz 0.3 Hz 0.1 Hz

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Spectral Envelope

Spectral envelope can identify the oscillations at 0.1 and 0.3 Hz.

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Optimal Scalings at 0.1 Hz

Tag Scaling Power

1 0.5839 93.3877 10 0.5402 79.9361 2 0.4241 49.2677 3 0.2857 22.3598 9 0.2803 21.5237 4 0.1617 7.1653 11 0.0214 0.1255 8 0.0093 0.0239 12 0.0093 0.0239 7 0.0092 0.0233 6 0.0089 0.0223 5 0.0088 0.0211

Magnitude of scaling for each variable Power at 0.1 Hz for each variable

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Scaling plot VS Power plot at 0.1Hz

Power plot Scaling plot

Both plots can help us to categorize variables.

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Scaling plot VS Power plot at 0.3Hz

Power plot Scaling plot

Both plots can help us to categorize variables.

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Application to industrial data

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Distillation plant example

Data from Mitsubishi Chemical Corporation 58 process variables from a distillation plant 3600 observations sampled at 1 min interval

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Overview of the data

Interested in oscillations with period 2.5 hours ( about 150 samples/cycle, or 1/150 = 0.0067)

tag 41- 58 tag 1- 20 tag 21- 40

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Spectral Envelope

0.0069, 144 samples/cycle

Spectral envelope detect the oscillation of concern with a period of 144 samples/cycle.

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Power plot at 0.0069 (144 samples/cycle)

Power plot

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pv-op plot

  • 1. This plot shows a pattern of valve stiction.
  • 2. Simple closed-loop plant test had confirmed that

the valve really has stiction problem.

  • 3. Further plant test and maintenance has been

scheduled during the next plant shutdown.

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Root cause Diagnosis

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Conclusion

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Conclusion

Spectral Envelop (SE) method has been

successfully applied to industrial data to identify the oscillation frequencies and categorize the variables that have the same

  • scillations.

Scaling plot and power plot provide useful

information on the root-cause of oscillations.

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Acknowledgement

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Acknowledgement

NSERC, Matrikon and ASRA for financial

support

  • Dr. Bhushan Gopaluni, Matrikon Inc.

Colleagues of Computer Process Control

(CPC) group at the University of Alberta

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Thank you!