Reliable fault diagnosis method using ensemble fuzzy ARTMAP based on improved Bayesian belief method

Neurocomputing

Volumn 133, Issue , 2014, Pages 309-316

  Jin, Min ^a   Li, Ren ^b   Xu, Zengbing ^b   Zhao, Xudong ^c,d  

^a HUNAN UNIVERSITY   (China)

^b Sany Smart Control Equipment Ltd   (China)

^c BOHAI UNIVERSITY   (China)

^d CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

FAM ensemble; Fault diagnosis; Fuzzy ARTMAP; Improved Bayesian belief method; Modified distance discriminant technique

Indexed keywords

FAILURE ANALYSIS; WAVELET ANALYSIS;

BAYESIAN; DIAGNOSIS PERFORMANCE; ENSEMBLE APPROACHES; FAM ENSEMBLE; FAULT DIAGNOSIS METHOD; FUZZY ARTMAP; MODIFIED DISTANCE DISCRIMINANT TECHNIQUE; ROLLING ELEMENT BEARING;

TIME SERIES ANALYSIS;

ACCURACY; ARTICLE; BAYES THEOREM; DATA EXTRACTION; DISCRIMINANT ANALYSIS; FUZZY ARTMAP ENSEMBLE APPROACH; INTERMETHOD COMPARISON; MACHINE LEARNING; MORLET WAVELET ANALYSIS METHOD; PRIORITY JOURNAL; RELIABILITY; SIGNAL PROCESSING; STATISTICAL ANALYSIS; TIME SERIES ANALYSIS;

EID: 84894572547     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.11.005     Document Type: Article

References (32)

1. Lin J., Qu L.S. Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J. Sound Vib. 2000, 234(1):135-148.
2. Tse P., Peng Y., Yam R Wavelet analysis and envelop detection for rolling element bearing fault diagnosis-their effectiveness and flexibilities. Trans. ASME: J. Vib. Acoust. 2001, 123(3):3-310.
3. Li Q., Wang T., Leng Y.G., Wang W., Wang G.F. Engineering signal processing based on adaptive step-changed stochastic resonance. Mech. Syst. Signal Process. 2007, 21(5):2267-2279.
4. He H.L., Wang T.Y., Leng Y.G., Zhang Y., Li Q. Study on non-linear filter characteristic and engineering application of cascaded bistable stochastic resonance system. Mech. Syst. Signal Process. 2007, 21(7):2740-2749.
5. Polycarpou M.M., Helmicki A.J. Automated fault detection and accommodation: a learning system approach. IEEE Trans. Syst. Man Cybern. 1995, 25:1447-1458.
6. Yang Y., Yu D.J., Cheng.J.S A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J. Sound Vib. 2006, 294:269-277.
7. F. Sun, Z.J. Wei. Rolling bearing fault diagnosis based on wavelet packet and RBF neural network, in: Proceeding of the 26th Chinese Control Conference, 2007, pp. 451-455.
8. Samanta B., Al-Balushi K.R., Al-Araimi S.A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng. Appl. Artif. Intell. 2003, 16:657-665.
9. Qiu J.B., Feng G., Yang J. A new design of delay-dependent robust H-infinity filtering for discrete-time T-S fuzzy systems with time-varying delay. IEEE Trans. Fuzzy Syst. 2009, 17(5):1044-1058.
10. Qiu J.B., Feng G., Gao H.J. Fuzzy-model-based piecewise H-infinity static output feedback controller design for networked nonlinear systems. IEEE Trans. Fuzzy Syst. 2010, 18(5):919-934.
11. Qiu J.B., Feng G., Gao H.J. Non-synchronized state estimation of multi-channel networked nonlinear systems with multiple packet dropouts via T-S fuzzy affine dynamic models. IEEE Trans. Fuzzy Syst. 2011, 19(1):75-90.
12. Zhao X.D., Zhang L.X., Shi P., Karimi.H. Novel stability criteria for T-S fuzzy systems. IEEE Trans. Fuzzy Syst. 2013, 99:110-111.
13. Javadpour R., Knapp G.M. A fuzzy neural network approach to machine condition monitoring. Comput. Ind. Eng. 2003, 45:323-330.
14. Tan S.C., Lim C.P. Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant. IEEE Trans. Energy Convers. 2004, 19:369-377.
15. Garperter G.A., Grossberg S., Markuzon N., et al. Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 1992, 3:698-712.
16. Georgiopoulos M., Fernlund H., Bebis G., Heileman G.L. Order of search in Fuzzy ART and Fuzzy ARTMAP: effect of the choice parameter. Neural Netw. 1996, 9(9):1541-1559.
17. Jin M., Zhou X., Zhang ZM., Tentzeris M.M. Short-term power load forecasting using grey correlation contest modeling. Expert Syst. Appl. 2012, 39(1):773-779.
18. Dagher I., Georgiopoulos M., Heileman G.L., et al. An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance. IEEE Trans. Neural Netw. 1999, 10(4):768-778.
19. Palaniappan R., Eswaran C. Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance. Appl. Soft Comput. J. 2009, 9:100-106.
20. Z.Y. Tang, X.A. Yan, Voting algorithm of fuzzy ARTMAP and its application to fault diagnosis, in: Fourth International Conference on Fuzzy System and Knowledge Discovery, 2007, pp. 535-538.
21. Loo C.K., Rao M.V.C. Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP. IEEE Trans. Knowl. Data Eng. 2005, 17(11):1589-1593.
22. Lehrman M., Rechester A.B., White R.B. Symbolic analysis of chaotic signals and turbulent fluctuation. Phys. Rev. Lett. 1997, 78:54-57.
23. Samanta B. Artificial neural networks and genetic algorithms for gear fault detection. Mech. Syst. Signal Process. 2004, 18:1273-1282.
24. Yang B.S., Kim K.J. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mech. Syst. Signal Process. 2006, 20:403-420.
25. Lei Y.G., He Z.J., Zi Y.Y. A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst. Appl. 2008, 35(4):1593-1600.
26. Liang J.N., Yang S., Adam W. Invariant optimal feature selection: a distance discriminant and feature ranking based solution. Pattern Recognit. 2008, 41(5):1429-1439.
27. Loparo K.A. Bearings Vibration Data Set 2003, Case Western Reserve University.
28. Wong M.L.D., Jack L.B., Nandi A.K. Modified self-organizing map for automated novelty detection applied to vibration signal monitoring. Mech. Syst. Signal Process. 2006, 20(3):593-610.
29. Zhang Y.P., Huang S.H., et al. Continuous wavelet grey moment approach for vibration analysis of rotating machinery. Mech. Syst. Signal Process. 2006, 20(5):1202-1220.
30. S.Z. Yang, Y. Wu, J.P. Xuan, Time Series Analysis in Engineering Application, Huazhong University of Science and Technology Press, 2007.
31. Lam L., Suen C. Optimal combination of pattern classifiers. Pattern Recognit. Lett. 1995, 16:945-954.
32. Chen L., Tang H.L. Improved computation of beliefs based on confusion matrix for combining multiple classifiers. Electron. Lett. 2004, 40(4):238-239.