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UNIVERSITA’ DI PISA Dipartimento di Ingegneria Chimica. Closed Loop Performance Monitoring: Automatic Diagnosis of Valve Stiction by means of a Technique based on Shape Analysis Formalism. ( 1,2 ) H. Manum, ( 1 ) C. Scali. ( 1 ) Chemical Process Control Laboratory ( CPCLab )

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slide1

UNIVERSITA’ DI PISA

Dipartimento di Ingegneria Chimica

Closed Loop Performance Monitoring:

Automatic Diagnosis of Valve Stiction

by means of a Technique

based on Shape Analysis Formalism

(1,2) H. Manum, (1)C. Scali

(1) Chemical Process Control Laboratory (CPCLab)

Department of Chemical Engineering

University of Pisa (I)

(2) Present: Norwegian University of Science and Technology

ANIPLA’06 Nov-13th -2006, Roma

slide2

Outline

  • CLPM issues & Valve Stiction
  • PCU: a CLPM System Architecture
  • Automatic Detection of Stiction: a Qualitative Shape Analysis Technique
  • Simulation & Application on Plant Data
  • Conclusions and Further Work

CLPM : Closed Loop Performance Monitoring

PCU: “Plant CheckUp” software package

slide3

Active research area !!!!

Closed Loop Performance Monitoring (CLPM)

  • Large importance for plant operation
  • Quality control, cost minimization
  • Fast detection of anomalies
  • Several unresolved aspects (Thornhill-Seborg’06,Qin’06 )
  • Academic:
  • Performance indexes for MIMO systems;
  • Technique for automatic diagnosis;
  • Disturbance propagation (& Root causes) in large scale plants
  • Practical:
  • Small plant perturbations;
  • “Optimal degree” of interaction with the operator;
  • Architectures: off-line vs. on-line
slide4

Causes of Oscillations

- Industrial plants: large number of loops

- Anomalies: appear as oscillations; which causes?

?

Different causes:

1) Improper Tuning

2) Valve Stiction

3) External perturbations

4) Interactions

Different actions:

1) Controller Re-tuning (Re-design)

2) Valve Maintenance (Stict. Compensation)

3) Upstream actions

4) Switch to MIMO control

slide5

Specific Problem addressed:

Stiction Detection

Reference scheme:

  • SP: set-point
  • OP: control action
  • PV: controlled variable
  • MV: manipulated variable
  • (MV not available in general)

Effect of Stiction :

Valve stuck: Fa<Fs (active force < static friction)

As soon as Fa>Fs: Jump and motion opposed only by dynamic friction.

As a consequence: cycling which causes oscillations in the response.

Models:

Theoretical: very complex (many parameters), values?

Empirical: much simpler (few parameters), less accurate

Empirical model adopted for simulation (Choudhury et al.’05)

slide6

The software package

  • Module 1: Hägglund technique
  • Module 2: If response is damped or sluggish the cause is poor tuning
  • Module 3: Loop subject to either
    • disturbance
    • stiction
    • no detection (needs closer analysis)
slide7

The software package

  • Module 3 uses three techniques for stiction detection (before current work)
  • Cross-correlation (Horch ‘99)
    • Cross-correlation function
  • Bi coherence (Choudhury et al ‘04)
    • Phase coupling
  • Relay technique (Rossi and Scali‘05)
    • Curve fitting
slide8

Stiction Detection from MV(OP)

Example 1: Loop behaving good (with setpoint change)

slide9

Stiction Detection from MV(OP)

Example 2: Loop suffering from stiction (with setpoint change)

slide10

Stiction Detection from MV(OP)

  • MV generally not acquired: exceptions:
  • flow control (FC): MVPV;
  • intelligent valves (field-bus)

Plots MV(OP):

No Stiction

Stiction

Plots PV(OP):

No Stiction

Stiction

Human eye: it seems an easy task to detect stiction from MV(OP) plots;

… But presence of noise & set point variations ..

The challenge is: automatic detection !!!

slide11

Automatic Recognition not so trivial:

  • Actual research: “Qualitative Shape Analysis”

Recent techniques (Re’03, Ya’06): Reliability?

Stiction Detection from MV(OP)

Presence of noise

Presence of set-points variations

slide12

DS

IS

1> 0.25 (=2/8) Stiction…

Yamashita Technique (Ya’06)

  • Basic idea:
  • Record MV and OP
  • Use derivatives to determine if signals are increasing (I), decreasing (D) or steady (S)
  • Combine in MV(OP) plot

OP,MV

I

S

D

time

8 possible combinations:

Simple stiction index:

1=(IS + DS)/(tot - SS );

MV

OP

slide13

3> 0.25  Stiction

Yamashita Technique (Ya’06)

Index 1 is not sharp enough for industrial data. Make a refined index by looking for patterns in MV(OP) plot

  • Count sequences in the data:
  • IS II, DS DD and IS SI, DS SD
  • 2=(IS II + DS DD + IS SI + DS SD )
  • /(tot - SS );
  • Index refined further by removing some limit cases:
  • 32
slide14

Easy implementation in any programming language

Implementation of the technique

  • Data acquisition: controller output (OP) & valve position / flow rate (MV)
  • Computation of time difference and normalization (mean and std dev.)
  • Quantization of each variable in three symbols: I, D, S
  • Description of qualitative movements by combination of symbols
  • Skip of SS sequences
  • Evaluation of index 1, counting IS and DS periods
  • Evaluation of the index 3by considering specific patterns
slide15

Application on simulated data

  • Simulation (Choudury’05 model), to investigate:
  • Threshold in symbolic representation
  • Length of time window
  • Effect of sampling time
  • Effect of noise
  • Effect of set point frequency
  • Conclusions
  • Some sensitivity to noise is shown
  • There is an optimal sampling time (noise dependent)
  • Indications degrades for high frequency:
    • seems OK for time-scale separation with factor 5 or more between the layers

And on plant data?

slide16

Application on plant data

  • Analysis of plant data & comparison with PCU results
  • N=216 PID loops, ( N’=167 FC!)
  • first results: robustness to noise (low), 2 hours of data are enough

Comparison of Stiction Verdicts: Yam: 32 (+ 8); PCU: 55 (+31)

  • Considerations:
  • (+8) can be explained: disregarded by PCU (no dominant frequency, required for bi-coherence method)
  • (-31): Stiction not detected ?
slide17

Application on plant data

  • Loop sticky not indicated by Yamashita:
  • Possible explanations:
  • Loops indicated as sticky: the two patterns were confirmed by visual inspection
  • In some cases: patterns distorted by noise or slave loops for advanced control systems
  • Other stiction patterns found (not considered by Yam)

MV

MV

OP

OP

May occur by changing

tunings or delay

Considered

slide18

Conclusions and further work

  • Favorable features:
  • Robustness to noise,
  • OK for set point variations
  • Quick computation:  implemented in software package (PCU)
  • Limitations:
  • Patterns not considered
  • Loops under advanced process control & noise
  • Further work:
  • Investigate different possible patterns
  • More information about valves
  • Specific experimentation
slide19

MV

OP

Not to be shown Cammini attrito?

Simulazione con Modello Choudury: cammini previsti

Valvola Diretta: Anti Orario

Valvola Inversa: Orario

MV

MV

OP

OP

Analisi Dati Industriali: cammini osservati  Movie ?

NO Attrito VD, AO VI, AO

MV

MV

OP

OP

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