Fuzzy multivariable gaussian evolving approach for fault detection and diagnosis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6178 LNAI, Issue , 2010, Pages 360-369

  Lemos, André ^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; APRIORI; DIAGNOSIS SYSTEMS; DYNAMIC SYSTEMS; FAULT DETECTION AND DIAGNOSIS; FAULT DIAGNOSIS; FUZZY CLASSIFIERS; GAUSSIANS; INCREMENTAL LEARNING; INDUCTION MACHINES; MULTI VARIABLES; NEW OPERATION MODES; PARTICIPATORY LEARNING; UNSUPERVISED CLUSTERING;

ARTIFICIAL INTELLIGENCE; DATA PROCESSING; DYNAMICAL SYSTEMS; FAILURE ANALYSIS; FUZZY SETS; KNOWLEDGE BASED SYSTEMS;

FAULT DETECTION;

EID: 77954877922     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14049-5_37     Document Type: Conference Paper

References (19)

1. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering 27(3), 293-311 (2003) (Pubitemid 36285962)
2. Montgomery, D.: Introduction to Statistical Quality Control, 4th edn. Wiley, Chichester (2001)
3. Dasgupta, D., Forrest, S.: Novelty detection in time series data using ideas from immunology. In: Neural Information Processing Systems (NIPS) Conference (1996)
4. Wong, M., Jack, L., Nandi, A.: Modified self-organising map for automated novelty detection applied to vibration signal monitoring. Mechanical Systems and Signal Processing 20(3), 593-610 (2006)
5. Timusk, M., Lipsett, M., Mechefske, C.K.: Fault detection using transient machine signals. Mechanical Systems and Signal Processing 22(7), 1724-1749 (2008)
6. Markou, M., Singh, S.: Novelty detection: A review part 1: Statistical approaches. Signal Processing 83, 2499-2521 (2003)
7. Angelov, P.P.: Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Springer, London (2002)
8. Kasabov, N., Filev, D.: Evolving intelligent systems: Methods, learning, & applications. In: International Symposium on Evolving Fuzzy Systems, pp. 8-18 (2006) (Pubitemid 46948083)
9. Yager, R.: A Model of Participatory Learning. IEEE Transactions on Systems Man and Cybernetics 20(5), 1229-1234 (1990)
10. Silva, L., Gomide, F., Yager, R.: Participatory learning in fuzzy clustering. In: The 14th IEEE International Conference on Fuzzy Systems, pp. 857-861 (2005)
11. Filev, D., Tseng, F.: Novelty detection based machine health prognostics. In: International Symposium on Evolving Fuzzy Systems, pp. 193-199 (2006)
12. Lughofer, E., Guardioler, C.: On-line fault detection with data-driven evolving fuzzy models. Control and Intelligent Systems 36(4), 307-317 (2008)
13. Wang, W., Vrbanek, J.: An evolving fuzzy predictor for industrial applications. IEEE Transactions on Fuzzy Systems 16(6), 1439-1449 (2008)
14. Lughofer, E.: Extensions of vector quantization for incremental clustering. Pattern Recognition 41(3), 995-1011 (2008); Part Special issue: Feature Generation and Machine Learning for Robust Multimodal Biometrics
15. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)
16. Schürmann, J.: Pattern classification: a unified view of statistical and neural approaches. John Wiley & Sons, Inc., New York (1996)
17. Miller, R.: Simultaneous statistical inference. McGraw-Hill, Inc., New York (1966)
18. D'Angelo, M.F., Palhares, R.M., Takahashi, R.H., Loschi, R.H., Baccarini, L.M., Caminhas, W.M.: Incipient fault detection in induction machine stator-winding using a fuzzy-bayesian change point detection approach. Applied Soft Computing (2009) (in Press, Corrected Proof)
19. Baccarini, L.M.R., de Menezes, B.R., Caminhas, W.M.: Fault induction dynamic model, suitable for computer simulation: Simulation results and experimental validation.Mechanical Systems and Signal Processing 24(1), 300-311 (2010)