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Closed Loop Performance Monitoring: Automatic Diagnosis of Valve Stiction

Explore automatic detection of valve stiction in industrial plants using a qualitative shape analysis technique. Simulate and apply the algorithm to plant data for anomaly detection. Practical application for performance monitoring and diagnosis.

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Closed Loop Performance Monitoring: Automatic Diagnosis of Valve Stiction

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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)

  6. 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)

  7. 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

  8. Stiction Detection from MV(OP) Example 1: Loop behaving good (with setpoint change)

  9. Stiction Detection from MV(OP) Example 2: Loop suffering from stiction (with setpoint change)

  10. 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 !!!

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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?

  16. 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 ?

  17. 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

  18. 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

  19. 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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