A "learning from models" cognitive fault diagnosis system

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

Volumn 7553 LNCS, Issue PART 2, 2012, Pages 305-313

  Alippi, Cesare ^a   Roveri, Manuel ^a   Trovò, Francesco ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

evolving clustering; fault cognitive diagnosis systems

Indexed keywords

COGNITIVE DIAGNOSIS; DIAGNOSIS SYSTEMS; EVOLVING CLUSTERING; FAULT DIAGNOSIS SYSTEMS; FAULT DICTIONARY;

COGNITIVE SYSTEMS; NEURAL NETWORKS; NONLINEAR DYNAMICAL SYSTEMS;

FAULT DETECTION;

EID: 84867685170     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33266-1_38     Document Type: Conference Paper

References (13)

1. Ljung, L.: Convergence analysis of parametric identification methods. IEEE Transactions on Automatic Control 23(5), 770-783 (1978)
2. Ljung, L., Caines, P.E.: Asymptotic normality of prediction error estimators for approximate system models. IEEE Decision and Control 17, 927-932 (1978)
3. Isermann, R.: Fault-diagnosis systems, an introduction from fault detection to fault tolerance. Springer (2006)
4. Fochem, M., Wischnewski, P., Hofmeier, R.: Qualitycontrolsystems on the productionline of tapedeckchassis using selforganizing feature maps. In: Proc. 1st European Symp. on Applications of Intelligent Technologies (1997)
5. Naresh, R., Sharma, V., Vashisthcv, M.: An integrated neural fuzzy approach for fault diagnosis of transformers. IEEE T. Power Del. 23(4), 2017-2024 (2008)
6. Kuo, H.C., Chang, H.K.: A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems. Engineering Applications of Artificial Intelligence 17, 919-930 (2004)
7. Joentgen, A., Mikenina, L., Weber, R., Zeugner, A., Zimmermann, H.J.: Automatic fault detection in gearboxes by dynamic fuzzy data analysis. Fuzzy Sets and Systems 105, 123-132 (1999)
8. Huang, G., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1-3), 489-501 (2006)
9. Schrauwen, B., Verstraeten, D., Campenhout, J.: An overview of reservoir computing: Theory, applications and implementations. E. S. on Artificial NNs, 471-482 (2007)
10. Nasraoui, O., Rojas, C.: Robust clustering for tracking noisy evolving data streams. In: Proc. SIAM Conf. Data Mining, pp. 618-622 (2006)
11. Song, Q., Kasabov, N.: ECM - A Novel On-line, Evolving Clustering Method and Its Applications. Found. of cognitive science, pp. 631-682. MIT Press (2001)
12. Angelov, P., Filev, D.P., Kasabov, N.: Evolving intelligent systems: Methodology and Applications, vol. 12. Wiley-IEEE Press (2010)
13. Johnson, R.A. and Wichern, D.W.: Applied multivariate statistical analysis. Prentice hall, Upper Saddle River (2002)