1. 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 CPCLab ( 1 ) Chemical Process Control Laboratory (CPCLab) Department of Chemical Engineering University of Pisa (I) ANIPLA’06 Nov-13 th -2006, Roma UNIVERSITA’ DI PISA Dipartimento di Ingegneria Chimica ( 2 ) Present: Norwegian University of Science and Technology

2. Manum & Scali CLPM, 2/18 ANIPLA’06 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 package CLPM : Closed Loop Performance Monitoring

3. Manum & Scali CLPM, 3/18 ANIPLA’06 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 Active research area !!!!

4. Manum & Scali CLPM, 4/18 ANIPLA’06 - 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 Causes of Oscillations

5. Manum & Scali CLPM, 5/18 ANIPLA’06 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) Empirical model adopted for simulation (Choudhury et al.’05)

6. Manum & Scali CLPM, 6/18 ANIPLA’06 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. Manum & Scali CLPM, 7/18 ANIPLA’06 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. Manum & Scali CLPM, 8/18 ANIPLA’06 Example 1: Loop behaving good (with setpoint change) Stiction Detection from MV(OP)

9. Manum & Scali CLPM, 9/18 ANIPLA’06 Example 2: Loop suffering from stiction (with setpoint change) Stiction Detection from MV(OP)

10. Manum & Scali CLPM, 10/18 ANIPLA’06 Human eye: it seems an easy task to detect stiction from MV(OP) plots;. … But presence of noise & set point variations.. automatic detection !!!  The challenge is: automatic detection !!! Stiction Detection from MV(OP) MV generally not acquired: exceptions: - flow control (FC): MV  PV; - intelligent valves (field-bus) Plots MV(OP): Stiction No Stiction Stiction No Stiction Plots PV(OP):

11. Manum & Scali CLPM, 11/18 ANIPLA’06 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)

12. Manum & Scali CLPM, 12/18 ANIPLA’06 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 IDS OP MV

13. Manum & Scali CLPM, 13/18 ANIPLA’06 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

14. Manum & Scali CLPM, 14/18 ANIPLA’06 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

15. Manum & Scali CLPM, 15/18 ANIPLA’06 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. Manum & Scali CLPM, 16/18 ANIPLA’06 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) Application on plant data Considerations: (+8) can be explained: disregarded by PCU (no dominant frequency, required for bi-coherence method) (-31): Stiction not detected ?

17. Manum & Scali CLPM, 17/18 ANIPLA’06 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 OP MV OP MV Considered May occur by changing tunings or delay

18. Manum & Scali CLPM, 18/18 ANIPLA’06 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 Conclusions and further work

19. Manum & Scali CLPM, 19/18 ANIPLA’06 Not to be shown Cammini attrito? Simulazione con Modello Choudury: cammini previsti Valvola Diretta: Anti OrarioValvola Inversa: Orario OP MV Analisi Dati Industriali: cammini osservati  Movie ? NO AttritoVD, AOVI, AO OP MV OP MV OP MV