1. Technical University of Lisbon 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. Technical University of Lisbon... Andrzej Marciniak contribution

3. Technical University of Lisbon 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. Technical University of Lisbon 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. Technical University of Lisbon 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. Technical University of Lisbon 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. Technical University of Lisbon Induced fuzzy sets the fuzzy membership functions are induced by the point-to-set affinity each category has associated a different β parameter

8. Technical University of Lisbon 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. Technical University of Lisbon GA vs HC No. expInitialFinalNo.calls (Method)fitnessfitnessclassifier 1 (GA)138.83147.98340 2 (GA)140.40149.54340 3 (GA)144.97151.26340 4 (GA)140.83149.82340 5 (GA)139.98151.08340 1 (HC)138.83149.54136 2 (HC)140.97150.59141 3 (HC)142.98151.50108 4 (HC)141.90151.30124 5 (HC)137.90149.78106

10. Technical University of Lisbon GA vs PSO No. expInitialFinalNo.calls (Method)fitnessfitnessclassifier 1 (GA)138.83147.98340 2 (GA)140.40149.54340 3 (GA)144.97151.26340 4 (GA)140.83149.82340 5 (GA)139.98151.08340 1 (PSO)124.18151.25100 2 (PSO)121.62150.03220 3 (PSO)117.65146.46160 4 (PSO)127.47151.38140 5 (PSO)134.07156.30140

11. Technical University of Lisbon 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. Technical University of Lisbon Results on DAMADICS benchmark – Step I The large overlapping between F3 and F6

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

14. Technical University of Lisbon Results on DAMADICS benchmark – Step I 5%10%15%20%25%30%35%40%45%50%55%60%65%70%75%80%85%90%95%100% F1NNNNNNNNN F7 F2NNNN F19F2 F3NNNF6NN F3 F1F3F6F3 F6NNNNNNNNN N F3 F7 F1 F10NNNNNF15F10F1 F10 F1F7 F11NNNNNN N N F13NF3 F13 F15N N NNN N NN N F16NNNNNNNNNNNNNN N F15F16 F17NNNN F18F3 NF18 F19NNF2 F19

15. Technical University of Lisbon 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