1.Diagnosis and Self-recovery
Engineering Research Center, Beijing University of Chemical Technology,
Beijing 100029, China; 2.Graduate School of Bioresources,
Mie University, Mie 514–8507, Japan; 3.College of Mechanical
Engineering, Jiamusi University, Jiamusi 154007, China;
Abstract：This paper reports several intelligent diagnostic approaches based on artificial neural network and fuzzy algorithm for plant machinery, such as the diagnosis method using the wavelet transform, rough sets, and fuzzy neural network; the diagnosis method based on the sequential inference and fuzzy neural network; the diagnosis approach by the possibility theory and certainty factor model; and the diagnosis method on the basis of the adaptive filtering technique and fuzzy neural network. These intelligent diagnostic methods have been successfully applied to condition diagnosis in different types of practical plant machinery.
Lin J, Qu L S. Feature extraction basedon Morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration, 2000, 234(1): 135–148 doi: 10.1006/jsvi.2000.2864
Liu B, Ling S F. On the selection of informativewavelets for machinery diagnosis. MechanicalSystems and Signal Processing, 1999, 13(1): 145–162 doi: 10.1006/mssp.1998.0177
Matuyama H. Diagnosisalgorithm. Journal of JSPE, 1991, 75(3): 35–37
Wang H Q, Chen P. Sequential condition diagnosisfor centrifugal pump system using fuzzy neural network. Neural Information Processing-Letters and Reviews, 2007, 11(3): 41–50 doi: 10.1007/s11063-007-9041-1
Pusey H C. Machinery condition monitoring. Journalof Sound and Vibration, 2000, 34(5): 6–7
Mitoma T, Wang H Q, Chen P. Fault diagnosis and condition surveillance for plantrotating machinery using partially-linearized neural network. Computers & Industrial Engineering, 2008, 55(4): 783–794 doi: 10.1016/j.cie.2008.03.002
Wang H Q, Chen P. Intelligent diagnosis methodfor a centrifugal pump using features of vibration signals. Neural Computing & Applications, 2009, 18(4): 397–405 doi: 10.1007/s00521-008-0192-4
Wang H Q, Chen P. Fault diagnosis of centrifugalpump using symptom parameters in frequency domain. Agricultural Engineering International: The CIGR E-journal, 2007, 11: 1–14
Saxena A, Saad A. Evolving an artificial neuralnetwork classifier for condition monitoring of rotating mechanicalsystems. Applied Soft Computing, 2007, 7(1): 441–454 doi: 10.1016/j.asoc.2005.10.001
Samanta B, Al-Balushi K R. Artificial neural networkbased fault diagnostics of rolling element bearings using time-domainfeatures. Mechanical Systems and SignalProcessing, 2003, 17(2): 317–328 doi: 10.1006/mssp.2001.1462
Li R Q, Chen J, Wu X. Fault diagnosis of rotating machinery using knowledge-basedfuzzy neural network. Applied Mathematicsand Mechanics, 2006, 27(1): 99–108 doi: 10.1007/s10483-006-0113-1
Alguindigue I E, Loskiewicz-Buczak A, Uhrig R E. Monitoring and diagnosis of rolling elementbearings using artificial neural networks. IEEE Transactions on Industrial Electronics, 1993, 40: 209–216 doi: 10.1109/41.222642
Samanta B, Al-Balushi K R, Al-Araimi S A. Artificial neural networks and genetic algorithm forbearing fault detection. Soft Computing, 2006, 10(3): 264–271 doi: 10.1007/s00500-005-0481-0
Christopher Bishop M I. Neural Networks for Pattern Recognition. UK: Oxford University Press, 1995
Pawlak Z. Roughsets. International Journal of ComputerInformation Science, 1982, 11: 344–356
Zhou X, Wang H Q, Chen P, Song J W. Diagnosis method for gear equipment by sequentialfuzzy neural network. In: Proceedings ofFifth International Symposium on Neural Networks, LNCS. Springer, 2008, 5624: 473–482
Pechon R, Jinyama H, Wang H Q. Fault diagnosis of rolling bearing using partially-linearizedneural network. Journal of the Societyof Plant Engineers Japan, 2007, 19(2): 133–141
Chen P, Toyota T. Sequential fuzzy diagnosisfor plant machinery. JSME InternationalJournal, Series C, 2003, 46(3): 1121–1129 doi: 10.1299/jsmec.46.1121
Wang H Q, Chen P. Sequential fuzzy diagnosisfor condition monitoring of rolling bearing based on neural network. In: Proceedings of Fifth International Symposiumon Neural Networks, LNCS. Springer, 2008, 5624: 284–293
Bendat J S. Probability function for random processes: prediction of peak, fatigue damage, and catastrophic failure. NASAReport CR33, 1969
Binaghi E, Luzi L, Madella P, Pergalani F, Rampint A. Slope instability zonation:a comparison between certainty factor and fuzzy dempster-shafer approaches. Natural Hazards, 1998, 17: 77–97 doi: 10.1023/A:1008001724538
Wang H Q, Chen P. Fault diagnosis for a rollingbearing used in a reciprocating machine by adaptive filtering techniqueand fuzzy neural network. WSEAS Transactionson Systems, 2008, 7(1): 1–6
Alexander S T. Adaptive signal processing: theory and applications. New York: Springer-Verlag, 1986
