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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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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


  • 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

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

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

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.


Theoretical: very complex (many parameters), values?

Empirical: much simpler (few parameters), less accurate

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

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)

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

Stiction Detection from MV(OP)

Example 1: Loop behaving good (with setpoint change)

Stiction Detection from MV(OP)

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

Stiction Detection from MV(OP)

  • MV generally not acquired: exceptions:

  • flow control (FC): MVPV;

  • intelligent valves (field-bus)

Plots MV(OP):

No Stiction


Plots PV(OP):

No 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 !!!

Stiction Detection from MV(OP)

Presence of noise

Presence of set-points variations



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






8 possible combinations:

Simple stiction index:

1=(IS + DS)/(tot - SS );



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

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

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?

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 ?

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)





May occur by changing

tunings or delay


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



Not to be shown Cammini attrito?

Simulazione con Modello Choudury: cammini previsti

Valvola Diretta: Anti Orario

Valvola Inversa: Orario





Analisi Dati Industriali: cammini osservati  Movie ?

NO Attrito VD, AO VI, AO