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Pattern Recognition Approach for Fault Diagnosis of DAMADICS Benchmark

Pattern Recognition Approach for Fault Diagnosis of DAMADICS Benchmark. Cosmin Bocaniala University “Dunarea de Jos” from Galati, Romania Andrzej Marciniak University of Zielona Gora, Poland Jose Sa da Costa Instituto Superior Tecnico, Lisbon, Portugal. Andrzej Marciniak contribution.

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Pattern Recognition Approach for Fault Diagnosis of DAMADICS Benchmark

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  1. Pattern Recognition Approach for Fault Diagnosis of DAMADICS Benchmark Cosmin Bocaniala University “Dunarea de Jos” from Galati, Romania Andrzej Marciniak University of Zielona Gora, Poland Jose Sa da Costa Instituto Superior Tecnico, Lisbon, Portugal

  2. ... Andrzej Marciniak contribution

  3. A Novel Fuzzy Classifier • fault diagnosis may be seen as a classification problem • building a map between symptoms space and the set of faulty states • two main advantages of the developed fuzzy classifier • the high accuracy with which it distinguishes between different categories • the fine precision of discrimination inside overlapping zones

  4. Previous work • three main directions of using fuzzy classifiers • neuro-fuzzy systems • robust to uncertainties and noise • collections of fuzzy rules • transparent symptoms-faults relationships via linguistic terms • represent normal state and each faulty state as fuzzy subsets of the symptoms space

  5. Point-to-point similarity • the similarity s(u,v) between two points is computed using a dissimilarity measure d(u,v) • the β parameter plays the role of a threshold value for the similarity measure • single or hybrid similarity measures

  6. Point-to-set similarity • the similarity measure between two points can be extended to a similarity measure between a data point and a set

  7. Induced fuzzy sets • the fuzzy membership functions are induced by the point-to-set affinity • each category has associated a different β parameter

  8. Computational aspects • the main computational issue is the search for the set of parameters that provide the best performance • genetic algorithms (slow) • hill climbing (fast) • particle swarm optimization (best!)

  9. GA vs HC No. exp Initial Final No.calls (Method) fitness fitness classifier 1 (GA) 138.83 147.98 340 2 (GA) 140.40 149.54 340 3 (GA) 144.97 151.26 340 4 (GA) 140.83 149.82 340 5 (GA) 139.98 151.08 340 1 (HC) 138.83 149.54 136 2 (HC) 140.97 150.59 141 3 (HC) 142.98 151.50 108 4 (HC) 141.90 151.30 124 5 (HC) 137.90 149.78 106

  10. GA vs PSO No. exp Initial Final No.calls (Method) fitness fitness classifier 1 (GA) 138.83 147.98 340 2 (GA) 140.40 149.54 340 3 (GA) 144.97 151.26 340 4 (GA) 140.83 149.82 340 5 (GA) 139.98 151.08 340 1 (PSO) 124.18 151.25 100 2 (PSO) 121.62 150.03 220 3 (PSO) 117.65 146.46 160 4 (PSO) 127.47 151.38 140 5 (PSO) 134.07 156.30 140

  11. Results on DAMADICS benchmark – Step I • the effects of six out of the 19 faults on this set of sensor measurements are not distinguishable from the normal behavior, {F4, F5, F8, F9, F12, F14} • also, there can be distinguished three groups of faults, {F3, F6}, {F7, F10}, and {F11, F15, F16}, that share similar effects on the measurements and, therefore, can be easily confound with faults in the same group.

  12. Results on DAMADICS benchmark – Step I The large overlapping between F3 and F6

  13. Results on DAMADICS benchmark – Step I The large overlapping between F7 and F10

  14. Results on DAMADICS benchmark – Step I

  15. Conclusions • advantages: high accuracy discrimination between different categories, and fine precision inside overlapping zones • fast parameters tuning using PSO • good performances on the DAMADICS benchmark, Step I

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