Adaptive fault detection and diagnosis using an evolving fuzzy classifier

Information Sciences

Volumn 220, Issue , 2013, Pages 64-85

  Lemos, Andre ^a   Caminhas, Walmir ^a   Gomide, Fernando ^b  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Adaptive fault detection and diagnosis; Evolving fuzzy systems; Participatory learning

Indexed keywords

ADAPTIVE FAULT DETECTION AND DIAGNOSIS; DATA STREAM; DIAGNOSIS SYSTEMS; EVOLVING FUZZY CLASSIFIERS; EVOLVING FUZZY SYSTEMS; FAULT DETECTION AND DIAGNOSIS; INCREMENTAL CLUSTERING; INCREMENTAL LEARNING; NEW OPERATION MODES; OPERATION MODE; OPERATION POINT; OPERATIONAL STATE; PARTICIPATORY LEARNING; PRIORI INFORMATION;

FUZZY SYSTEMS; KNOWLEDGE ENGINEERING;

FAULT DETECTION;

EID: 84864839130     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2011.08.030     Document Type: Conference Paper

References (52)

1. J. Abonyi, R. Babuska, and F. Szeifert Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 32 2002 612 621
2. C. Angeli, and A. Chatzinikolaou On-line fault detection techniques for technical systems: a survey International Journal of Computer Science & Applications 1 2004 12 30
3. P. Angelov, and D. Filev An approach to Online identification of Takagi-Sugeno fuzzy models IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 34 2004 484 498
4. P.P. Angelov, and X. Zhou Evolving fuzzy-rule-based classifiers from data streams IEEE Transactions on Fuzzy Systems 16 2008 1462 1475
5. M. Bartys, R. Patton, M. Syfert, S. de las Heras, and J. Quevedo Introduction to the damadics actuator fdi benchmark study Control Engineering Practice 14 2006 577 596
6. C.D. Bocaniala, and J.S. da Costa Application of a novel fuzzy classifier to fault detection and isolation of the damadics benchmark problem Control Engineering Practice 14 2006 653 669 (A benchmark study of fault diagnosis for an industrial actuator)
7. L. Chen, and T. Wang Artificial neural networks to classify mean shifts from multivariate chi(2) chart signals Computers & Industrial Engineering 47 2004 195 205
8. H.J. Cho, and J.K. Park An expert system for fault section diagnosis of power systems using fuzzy relations IEEE Transactions on Power Systems 12 1997 342 347
9. M.F. D'Angelo, R.M. Palhares, R.H. Takahashi, R.H. Loschi, L.M. Baccarini, and W.M. Caminhas Incipient fault detection in induction machine stator-winding using a fuzzy-bayesian change point detection approach Applied Soft Computing 11 2011 179 192
10. D. Dasgupta, S. Forrest, Novelty detection in time series data using ideas from immunology, in: Neural Information Processing Systems (NIPS) Conference, 1996.
11. Y. Dong, Z. Sun, and H. Ha A cosine similarity-based negative selection algorithm for time series novelty detection Mechanical Systems and Signal Processing 20 2006 1461 1472
12. T. Fawcett An introduction to roc analysis Pattern Recognition Letters 27 2006 861 874 (ROC analysis in pattern recognition)
13. D. Filev, F. Tseng, Novelty detection based machine health prognostics, in: 2006 International Symposium on Evolving Fuzzy Systems, 2006, pp. 193-199.
14. M. Hisada, S. Ozawa, K. Zhang, and N. Kasabov Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems Evolving Systems 1 2010 17 27
15. M. Iri, K. Aoki, E. O'Shima, and H. Matsuyama An algorithm for diagnosis of system failures in the chemical process Computers & Chemical Engineering 3 1979 489 493
16. R. Isermann Process fault detection based on modeling and estimation methods - A survey Automatica 20 1984 387 404
17. Y. Iwasaki, and H.A. Simon Causality in device behavior Artificial Intelligence 29 1986 3 32
18. R.E. Kalman A new approach to linear filtering and prediction problems Transactions of the ASME Journal of Basic Engineering 1960 35 45
19. N. Kasabov, D. Filev, Evolving intelligent systems: methods, learning, & applications, in: 2006 International Symposium on Evolving Fuzzy Systems, 2006, pp. 8-18.
20. N. Kasabov, and Q. Song DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction IEEE Transactions on Fuzzy Systems 10 2002 144 154
21. E. Kim, M. Park, S. Kim, and M. Park A transformed input-domain approach to fuzzy modeling IEEE Transactions on Fuzzy Systems 6 1998 596 604
22. C.A. Laurentys, R.M. Palhares, and W.M. Caminhas Design of an artificial immune system based on danger model for fault detection Expert Systems with Applications 37 2010 5145 5152
23. C.A. Laurentys, R.M. Palhares, and W.M. Caminhas A novel artificial immune system for fault behavior detection Expert Systems with Applications 38 2011 6957 6966
24. W.S. Lee, D.L. Grosh, F.A. Tillman, and C.H. Lie Fault tree analysis, methods and applications - A review IEEE Transactions on Reliability 34 1985 194 203
25. A. Lemos, W. Caminhas, and F. Gomide Fuzzy multivariable gaussian evolving approach for fault detection and diagnosis E. Hullermeier, R. Kruse, F. Hoffmann, Computational Intelligence for Knowledge-Based Systems Design Lecture Notes in Computer Science vol. 6178 2010 Springer/ Heidelberg Berlin 360 369
26. A. Lemos, F. Gomide, and W. Caminhas Multivariable gaussian evolving fuzzy modeling system IEEE Transactions on Fuzzy Systems 19 2011 91 104
27. D.G. Luenberger Observing the state of a linear system IEEE Transactions on Military Electronics 8 1964 74 80
28. E. Lughofer Extensions of vector quantization for incremental clustering Pattern Recognition 41 2008 995 1011 (Part Special issue: Feature Generation and Machine Learning for Robust Multimodal Biometrics)
29. E. Lughofer On dynamic soft dimension reduction in evolving fuzzy classifiers E. Hullermeier, R. Kruse, F. Hoffmann, Computational Intelligence for Knowledge-Based Systems Design 2010 Springer 79 88
30. E. Lughofer On-line incremental feature weighting in evolving fuzzy classifiers Fuzzy Sets and Systems 163 2011 1 23
31. E. Lughofer, P. Angelov, X. Zhou, Evolving single- and multi-model fuzzy classifiers with flexfis-class, in: IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007, 2007, pp. 1-6.
32. E.C.G. Lughofer On-line fault detection with data-driven evolving fuzzy models Control and Intelligent Systems 36 2008 307 317
33. E.D. Lughofer FLEXFIS: a robust incremental learning approach for evolving takagi-sugeno fuzzy models IEEE Transactions on Fuzzy Systems 16 2008 1393 1410
34. M. Markou, and S. Singh Novelty detection: a review part 1: statistical approaches Signal Processing 83 2003 2499 2521
35. M. Markou, and S. Singh Novelty detection: a review part 2: neural networks based approaches Signal Processing 83 2003 2481 2497
36. N. Mehranbod, M. Soroush, and C. Panjapornpon A method of sensor fault detection and identification Journal of Process Control 15 2005 321 339
37. L.F. Mendonca, J.M.C. Sousa, and J.M.G.S. da Costa An architecture for fault detection and isolation based on fuzzy methods Expert Systems with Applications 36 2009 1092 1104
38. D. Montgomery Introduction to Statistical Quality Control fourth ed. 2001 Wiley
39. S. Ozawa, S. Pang, and N. Kasabov Online feature extraction for evolving intelligent systems P. Angelov, D. Filev, N. Kasabov, Evolving Intelligent Systems: Methodology and Applications 2010 Wiley-Interscience/IEEE Press
40. A. Papoulis Probability, Random Variables, and Stochastic Processes second ed. 1984 McGraw-Hill, Inc.
41. F. Previdi, and T. Parisin Model-free actuator fault detection using a spectral estimation approach: the case of the damadics benchmark problem Control Engineering Practice 14 2006 635 644
42. V. Rajagopalan, and A. Ray Symbolic time series analysis via wavelet-based partitioning Signal Processing 86 2006 3309 3320
43. J.d.J. Rubio Sofmls: online self-organizing fuzzy modified least-squares network IEEE Transactions on Fuzzy Systems 17 2009 1296 1309
44. B. Samanta Gear fault detection using artificial neural networks and support vector machines with genetic algorithms Mechanical Systems and Signal Processing 18 2004 625 644
45. M. Timusk, M. Lipsett, and C.K. Mechefske Fault detection using transient machine signals Mechanical Systems and Signal Processing 22 2008 1724 1749
46. V. Venkatasubramanian, R. Rengaswamy, S. Kavuri, and K. Yin A review of process fault detection and diagnosis Part I: Quantitative model-based methods Computers & Chemical Engineering 27 2003 293 311
47. V. Venkatasubramanian, R. Rengaswamy, S. Kavuri, and K. Yin A review of process fault detection and diagnosis Part II: Quantitative model and search strategies Computers & Chemical Engineering 27 2003 313 326
48. V. Venkatasubramanian, R. Rengaswamy, S. Kavuri, and K. Yin A review of process fault detection and diagnosis Part III: Process history based methods Computers & Chemical Engineering 27 2003 327 346
49. W. Wang, and J. Vrbanek An evolving fuzzy predictor for industrial applications IEEE Transactions on Fuzzy Systems 16 2008 1439 1449
50. M. Wong, L. Jack, and A. Nandi Modified self-organising map for automated novelty detection applied to vibration signal monitoring Mechanical Systems and Signal Processing 20 2006 593 610
51. R. Yager A model of participatory learning IEEE Transactions on Systems Man and Cybernetics 20 1990 1229 1234
52. R.R. Yager Extending the participatory learning paradigm to include source credibility Fuzzy Optimization and Decision Making 6 2007 85 97