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Fuzzy Logic Application for Fault Isolation of Actuators

Fuzzy Logic Application for Fault Isolation of Actuators. April 5-7, 2004 DAMADICS 2004 5-th DAMADICS Workshop on Integration of Qualitative/Quantitative Methods for Fault Diagnosis Presentation of Final Results Łagów/Poland. Introduction Fault detection Fuzzy residual evaluation

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Fuzzy Logic Application for Fault Isolation of Actuators

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  1. Fuzzy Logic Application for Fault Isolation of Actuators April 5-7, 2004 DAMADICS 2004 5-th DAMADICS Workshop on Integration of Qualitative/Quantitative Methods for Fault Diagnosis Presentation of Final Results Łagów/Poland • Introduction • Fault detection • Fuzzy residual evaluation • Fuzzy reasoning rules • Fault isolation algorithm • Industrial benchmark problem • Final remarks Jan Maciej Kościelny Michał Bartyś Paweł Rzepiejewski Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  2. parity equation - (Massoumia and Van der Velde, 1988; Mediavilla et al.,1997) • unknown input observer Phatak and Wiswandham, 1988) • extended Kalman filter (Oehler et al., 1997) • signal analysis (Deibert, 1994) • fuzzy logic (Kościelny and Bartyś, 1997; 2000) • b-spline (Benkhedda and Patton, 1997) • spectral analysis (Previdi and Parisini, 2003) • pattern recognition (Marciniak et al., 2003) • structural analysis (Frisk et al., 2003) • timed automata (Lunze and Supravatanakul, 2003) IntroductionActuator FDI approaches Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  3. IntroductionIntelligent actuators supporting auto diagnostic andauto validation functions • Bayart and Staroswiecki, 1991 • Isermann and Raab, 1993 • Kościelny and Bartyś, 1997 • Yang und Clarke, 1997; 1999 • Tombs, 2002 Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  4. IntroductionFuzzy logic applications for development of FDI algorithms of actuators • Frank, 1994 • Garcia et al., 1997 • Kościelny et al. , 1999 • Kościelny 1999; 2001 • Sędziak, 2001 • Calado et al. 2003 • Korbicz et al. 2004 • Yang und Clarke, 1997; 1999 • Tombs, 2002 Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  5. The set of diagonostic signals Set of residuals S={sj: j=1,2,...,J } R={rj: j=1,2,...,J } + Residual generation Fuzzy residual evaluation - Fuzzy reasoning Set of pairs: <fault, fault certainty > Process data set X={xi: i=1,2,...,I } <fk,k> IntroductionModel based fuzzy FDI system schemeFuzzy approach Fault isolation Fault detection Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  6. Actuator fault detection 10 20 30 40 50 Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  7. Fault detectionFeed forward perceptron neural networks (MLP) MA models because: - easiness of learning - satisfactory modeling errors ARMA models not because: - no significant improvement of model quality - ability of fault learning Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  8. Fault detectionExamples of modelling results achieved Exemplification of flow rate model (3) quality in fault free state (normal process state). Flow rate in technical units [t/h] versus time in [s] is shown. Significant (ca. 50%) flow drop was observed. - easiness of learning Illustration of fault sensitivity of the flow rate model (3). Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  9. 1 -1 m (r ) 0 nj 0.50 m (r ) 1 nj r nj Fuzzy residual evaluationTri-valued fuzzy residuum evaluation (idea) m (r ) 1.0 nj 0 0.75 0.25 -T T 0.0 nj -1.0 1.0 nj Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  10. 10 Fuzzy fault symptom is the k dimensional fuzzy set such that for each residual rj assign k-plets 20Fuzzy multiple-valued symptom • where: • - membership function of the j-th residual to the fuzzy set vji • Vj – the set of fuzzy values of j-th fuzzy diagnostic signal Definitions Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  11. Setting parameters of membership functionsStatistical approach • Examples of experimental histograms of residual of flow rate model of control valve of Actuator Benchmark Problem in fault free process state. Additional filtering technique (low pass moving average filer) applied for the instrumentation measurements may reduce the span of residual distribution (right chart). Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  12. Setting parameters of membership functionsAbrupt fault occurrence • Examples of histograms of residual of flow rate model of control valve of Actuator Benchmark Problem in faulty process state. The occurrence of abrupt fault is documented. Additional filtering technique (low pass moving average filter) applied for the instrumentation measurements increase separation between neighbourhood residual values in fault free and faulty states an lower the number of intermediate residual values (right chart). Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  13. DGN 2 DGN 1 DGN i DGN n snj s1j s2j sij Fuzzy reasoning rules Reformulation Example of rule isolating fault f1 Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  14. Reference values of diagnostic signals used for fault reasoning Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  15. Diagnostic matrix Actual signals PATTERN RECOGNITION F/S f1 ... fk ... fK Diagnostic signals S1 Vk1 S1 … ... ... Sj Vkj Sj … ... ... Fault signature sJ VkJ VKJ SJ Rules Parallel reasoning scheme Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  16. R( f ) DGN 10 Fulfillment degrees of rules’ premises 20 k-th fuzzy rule output by fuzzy symptom sj Operators 30 T-norm for fuzzy fk output 40Diagnosis Fault isolation algorithm Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  17. Industrial benchmark problemElementary diagnosis Theoretical diagnosis accuracy daccti L - the number of faults indicated in ist elementary diagnosis, for DGN0 the fault free state (OK) is also included Theoretical mean diagnosis accuracy dacctm N is a number of elementary diagnosis Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  18. Industrial benchmark problemFault f16 (supply air pressure drop) (OK - state)

  19. Industrial benchmark problemFault f18 (partly opened bypass valve)

  20. Industrial benchmark problemSummary of experimental FDI performance indices of industrial benchmark Remarks: 1. detection moment was captured when OK state certainty degree drop down below 0.75 2. Isolation moment was captured when fault certainty degree rise above 0.25time tdt - detection time rfd - false detection rate tdm - detection moment tirt - fault detection recovery time tit - isolation time rfi - false isolation rate rti - true isolation rate tim - isolation moment tirt - fault isolation recovery time Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

  21. Final remarks • Simple and practicable fuzzy fault isolation approach was presented. • The reasoning fuzzy system consists of fuzzyfication and inference procedures. Defuzzyfication is not being applied. • Diagnosis are pointing out particular faults related with the fault certainty degrees. • Improved robustness against measurement noise and model uncertainty. • Applicable for on-line diagnostics of industrial processes • Symptom uncertainty allows to improve the overall tolerance of diagnostic system on the disturbances and. • Fault certainty degree has no direct transformation onto the fault probability. It plays the auxiliary role and serves as an approximate estimation of the fault occurrence degree. • Fault certainty value depends on the selection of parameters of fuzzyfication process, method of fuzzy inferring and modelling quality • Fault certainty degree may be thought as practically acceptable because of intuitive acceptance and easy graphical interpretation. Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems, FP5 DAMADICS Project

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