Machine health condition prediction via online dynamic fuzzy neural networks

Engineering Applications of Artificial Intelligence

Volumn 35, Issue , 2014, Pages 105-113

  Pan, Yongping ^a   Er, Meng Joo ^b   Li, Xiang ^c   Yu, Haoyong ^a   Gouriveau, Rafael ^d  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

^d FEMTO ST INSTITUTE   (France)

Author keywords

Fuzzy neural network; Machine health condition; Multi step prognosis; Non stationary time series; Real time learning; Self organization

Indexed keywords

FORECASTING; FUZZY INFERENCE; FUZZY LOGIC; FUZZY RULES; HEALTH; LEARNING SYSTEMS; REAL TIME CONTROL;

MACHINE HEALTH; MULTI-STEP; NON-STATIONARY TIME SERIES; REAL-TIME LEARNING; SELF ORGANIZATIONS;

FUZZY NEURAL NETWORKS;

EID: 84906075323     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2014.05.015     Document Type: Article

References (48)

1. M.F. Azeem, M. Hanmandlu, and N. Ahmad Structure identification of generalized adaptive neuro-fuzzy inference systems IEEE Trans. Fuzzy Syst. 11 5 2003 666 681
2. C.S. Chen Robust self-organizing neural-fuzzy control with uncertainty observer for MIMO nonlinear systems IEEE Trans. Fuzzy Syst. 19 4 2011 694 706
3. C.C. Chen, B. Zhang, G. Vachtsevanos, and M. Orchard Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering IEEE Trans. Ind. Electron. 58 9 2011 4353 4364
4. D. Dovzan, and I. Skrjanc Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes ISA Trans. 50 2 2011 159 169
5. M.J. Er, and C. Deng Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning IEEE Trans. Syst. Man Cybern. Part B Cybern. 34 3 2004 1478 1489
6. M.J. Er, and Y. Zhou Automatic generation of fuzzy inference systems via unsupervised learning Neural Netw. 21 10 2008 1556 1566
7. M.J. Er, Z.G. Li, H.N. Cai, and Q. Chen Adaptive noise cancellation using enhanced dynamic fuzzy neural networks IEEE Trans. Fuzzy Syst. 13 3 2005 331 342
8. N. Garcia-Pedrajas, C. Hervag-Martinez, and J. Munoz-Perez COVNET a cooperative coevolutionary model for evolving artificial neural networks IEEE Trans. Neural Netw. 14 3 2003 575 596
9. M. Gasperin, D. Juricic, P. Boskoski, and J. Vizintin Model-based prognostics of gear health using stochastic non-linear dynamical models Int. J. Cond. Monit. 1 2 2011 67 72
10. M. Gasperin, D. Juricic, P. Boskoski, and J. Vizintin Model-based prognostics of gear health using stochastic dynamical models Mech. Syst. Signal Process. 25 2 2011 537 548
11. G.B. Huang, Q.Y. Zhu, and C.K. Siew Extreme learning machine theory and applications Neurocomputing 70 1-3 2006 489 501
12. G.B. Huang, L. Chen, and C.K. Siew Universal approximation using incremental constructive feedforward networks with random hidden nodes IEEE Trans. Neural Netw. 17 4 2006 879 892
13. A.K.S. Jardine, D.M. Lin, and D. Banjevic A review on machinery diagnostics and prognostics implementing condition-based maintenance Mech. Syst. Signal Process. 20 7 2006 1483 1510
14. C.F. Juang, and C.T. Lin An online self-constructing neural fuzzy inference network and its applications IEEE Trans. Fuzzy Syst. 6 1 1998 12 32
15. G. Leng, G. Prasad, and T.M. McGinnity An on-line algorithm for creating self-organizing fuzzy neural networks Neural Netw. 17 10 2004 1477 1493
16. G. Li, M. Liu, and M. Dong A new online learning algorithm for structure-adjustable extreme learning machine Comput. Math. Appl. 60 3 2010 377 389
17. X. Li, M.J. Er, B.S. Lim, J.H. Zhou, O.P. Gan, and L. Rutkowski Fuzzy regression modeling for tool performance prediction and degradation detection Int. J. Neural Syst. 20 5 2010 405 419
18. C.C. Lin, and H.Y. Tseng A neural network application for reliability modelling and condition-based predictive maintenance Int. J. Adv. Manuf. Technol. 25 1-2 2005 174 179
19. J. Lin, and R.J. Lian Stability indices for a self-organizing fuzzy controlled robot a case study Eng. Appl. Artif. Intell. 23 6 2010 1019 1034
20. Y. Lin, and G.A. Cunningham III A new approach to fuzzy-neural system modeling IEEE Trans. Fuzzy Syst. 3 2 1995 190 198
21. F.J. Lin, C.H. Lin, and P.H. Shen Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive IEEE Trans. Fuzzy Syst. 9 5 2001 751 759
22. J. Liu, W. Wang, and F. Golnaraghi A multi-step predictor with a variable input pattern for system state forecasting Mech. Syst. Signal Process. 23 5 2009 1586 1599
23. E. Lughofer, J.L. Bouchot, and A. Shaker On-line elimination of local redundancies in evolving fuzzy systems Evol. Syst. 2 3 2011 165 187
24. S. Mitra, and Y. Hayashi Neuro-fuzzy rule generation survey in soft computing framework IEEE Trans. Neural Netw. 11 3 2000 748 768
25. Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso, E.; Morello, B.; Zerhouni, N.; Varnier, C.; 2012. PRONOSTIA: an experimental platform for bearings accelerated degradation tests. In: Proceedings of IEEE International Conference on Prognostics Health Management, Denver, CO, 2012, pp. 1-8.
26. H. Niska, T. Hiltunen, A. Karppinen, J. Ruuskanen, and M. Kolehmainen Evolving the neural network model for forecasting air pollution time series Eng. Appl. Artif. Intell. 17 2 2004 159 167
27. H az tracking control Eng. Appl. Artif. Intell. 24 7 2011 1174 1185
28. Y.P. Pan, M.J. Er, D.P. Huang, and Q.R. Wang Adaptive fuzzy control with guaranteed convergence of optimal approximation error IEEE Trans. Fuzzy Syst. 19 5 2011 807 818
29. Y.P. Pan, X.Y. Hu, M.J. Er, X. Li, and R. Gouriveau Bearing condition prediction using enhanced online learning fuzzy neural networks A.Y.C. Nee, B. Song, S.K. Ong, Re-engineering Manufacturing for Sustainability 2013 Springer Singapore 175 182
30. R.P. Prado, S. Garcia-Galan, A.J. Yuste, and J.E.M. Exposito A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing Eng. Appl. Artif. Intell. 23 7 2010 1072 1082
31. M. Pratama, M.J. Er, X. Li, R.J. Oentaryo, E. Lughofer, and I. Arifin Data driven modeling based on dynamic parsimonious fuzzy neural network Neurocomputing 110 2013 18 28
32. H.J. Rong, N. Sundararajan, G.B. Huang, and P. Saratchandran Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction Fuzzy Sets Syst. 157 9 2006 1260 1275
33. H.J. Rong, G.B. Huang, N. Sundararajan, and P. Saratchandran Online sequential fuzzy extreme learning machine for function approximation and classification problems IEEE Trans. Syst. Man Cybern. Part B Cybern. 39 4 2009 1067 1072
34. A. Sokolowski On some aspects of fuzzy logic application in machine monitoring and diagnostics Eng. Appl. Artif. Intell. 17 4 2004 429 437
35. H. Soleimani-B, C. Lucas, and B.N. Araabi Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling Evol. Syst. 1 1 2010 59 71
36. Z.G. Tian, and M.J. Zuo Health condition prediction of gears using a recurrent neural network approach IEEE Trans. Reliab. 59 4 2010 700 705
37. V.T. Tran, B.S. Yang, and A.C.C. Tan Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems Expert Syst. Appl. 36 5 2009 9378 9387
38. S.G. Tzafestas, and K.C. Zikidis NeuroFAST on-line neuro-fuzzy ART-based structure and parameter learning TSK model IEEE Trans. Syst. Man Cybern. Part B Cybern. 31 5 2001 797 802
39. G.J. Vachtsevanos, F.L. Lewis, M. Roemer, A. Hess, and B. Wu Intelligent Fault Diagnosis and Prognosis for Engineering Systems 2006 John Wiley & Sons Hoboken, NJ
40. W. Wang An adaptive predictor for dynamic system forecasting Mech. Syst. Signal Process. 21 2 2007 809 823
41. W. Wang, M.F. Golnaraghi, and F. Ismail Prognosis of machine health condition using neuro-fuzzy systems Mech. Syst. Signal Process. 18 4 2004 813 831
42. S.J. Wu, N. Gebraeel, M.A. Lawley, and Y. Yih A neural network integrated decision support system for condition-based optimal predictive maintenance policy IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37 2 2007 226 236
43. S.Q. Wu, M.J. Er, and Y. Gao A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks IEEE Trans. Fuzzy Syst. 9 4 2001 578 594
44. X. Yao Evolving artificial neural networks Proc. IEEE 87 9 1999 1423 1447
45. J.B. Yu, S.J. Wang, and L.F. Xi Evolving artificial neural networks using an improved PSO and DPSO Neurocomputing 71 4-6 2008 1054 1060
46. M. Yu, D. Wang, M. Luo, and L. Huang Prognosis of hybrid systems with multiple incipient faults augmented global analytical redundancy relations approach IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41 3 2011 540 551
47. M. Yu, D. Wang, and M. Luo Model based prognosis for hybrid systems with mode-dependent degradation behaviors IEEE Trans. Ind. Electron. 61 1 2014 546 554
48. F.G. Zhao, J. Chen, L. Guo, and X.L. Li Neuro-fuzzy based condition prediction of bearing health J. Vib. Control 15 7 2009 1079 1091