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 (CPCLabCPCLab) Department of Chemical Engineering University of Pisa (I)

ANIPLA’06 Nov-13th -2006, Roma

UNIVERSITA’ DI PISA

Dipartimento di Ingegneria Chimica

(2) Present: Norwegian University of Science and Technology

Manum & Scali

ANIPLA’06 CLPM, 2/18

1. CLPM issues & Valve Stiction

2. PCU: a CLPM System Architecture

3. Automatic Detection of Stiction: a Qualitative

Shape Analysis Technique

4. Simulation & Application on Plant Data

5. Conclusions and Further Work

Outline

PCU: “Plant CheckUp” software packageCLPM : Closed Loop Performance Monitoring

Manum & Scali

ANIPLA’06 CLPM, 3/18

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-lineActive research area !!!!

Manum & Scali

ANIPLA’06 CLPM, 4/18

- Industrial plants: large number of loops - Anomalies: appear as oscillations; which causes?

Different causes:1) Improper Tuning 2) Valve Stiction3) External perturbations4) Interactions

Different actions:1) Controller Re-tuning (Re-design)2) Valve Maintenance (Stict. Compensation)3) Upstream actions 4) Switch to MIMO control

Causes of Oscillations

Manum & Scali

ANIPLA’06 CLPM, 5/18

Specific Problem addressed: Stiction Detection

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

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

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

Manum & Scali

ANIPLA’06 CLPM, 6/18

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)

Manum & Scali

ANIPLA’06 CLPM, 7/18

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

Manum & Scali

ANIPLA’06 CLPM, 8/18

Example 1: Loop behaving good (with setpoint change)

Stiction Detection from MV(OP)

Manum & Scali

ANIPLA’06 CLPM, 9/18

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

Stiction Detection from MV(OP)

Manum & Scali

ANIPLA’06 CLPM, 10/18

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 !!!automatic detection !!!

Stiction Detection from MV(OP)

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

Plots MV(OP):

Stiction No Stiction

Stiction No Stiction

Plots PV(OP):

Manum & Scali

ANIPLA’06 CLPM, 11/18

Automatic Recognition not so trivial: Actual research: “Qualitative Shape Analysis” Recent techniques (Re’03, Ya’06): Reliability?

Presence of noise

Presence of set-points variations

Stiction Detection from MV(OP)

Manum & Scali

ANIPLA’06 CLPM, 12/18

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 ); ISDS

1 > 0.25 (=2/8) Stiction…

OP,MV

time

I DS

OP

MV

Manum & Scali

ANIPLA’06 CLPM, 13/18

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

3 > 0.25 Stiction

Manum & Scali

ANIPLA’06 CLPM, 14/18

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 3 by considering specific patterns

Easy implementation in any programming language

Manum & Scali

ANIPLA’06 CLPM, 15/18

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?

Manum & Scali

ANIPLA’06 CLPM, 16/18

Analysis of plant data & comparison with PCU resultsN=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)

Application on plant data

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

Manum & Scali

ANIPLA’06 CLPM, 17/18

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)

Application on plant data

IS

DI

DS

ID

IS

DI

DS

ID

OP

MV

IS

II

DS

DD

IS

II

DS

DD

OP

MV

Considered May occur by changing tunings or delay

Manum & Scali

ANIPLA’06 CLPM, 18/18

• Favorable features:Robustness to noise,OK for set point variationsQuick 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

Conclusions and further work

Manum & Scali

ANIPLA’06 CLPM, 19/18

Not to be shown Cammini attrito?

Simulazione con Modello Choudury: cammini previsti

Valvola Diretta: Anti Orario Valvola Inversa: Orario

IS

II

DS

DD

IS

II

DS

DDDI

ID

DS

IS

DIID

DS

IS

OP OP

MV MV

Analisi Dati Industriali: cammini osservati Movie ?NO Attrito VD, AO VI, AO

IS

II

DS

DD

IS

II

DS

DD

OP

MV

IS

DI

DS

ID

IS

DI

DS

ID

OP

MV

OP

MV