Post on 02-Feb-2016
description
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
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ID
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OP
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