A new approach to intelligent fault diagnosis of rotating machinery

This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy entropies, are extracted to acquire more fault characteristic information. Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into ANFIS to identify different abnormal cases. The proposed approach is applied to fault diagnosis of rolling element bearings, and testing results show that the proposed approach can reliably recognise different fault categories and severities. Moreover, the effectiveness of the proposed feature selection method is also demonstrated by the testing results.

[1]  Tao Han,et al.  ART–KOHONEN neural network for fault diagnosis of rotating machinery , 2004 .

[2]  Bo-Suk Yang,et al.  Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals , 2006 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Asoke K. Nandi,et al.  Modified self-organising map for automated novelty detection applied to vibration signal monitoring , 2006 .

[5]  Alireza Sadeghian,et al.  Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System , 2006 .

[6]  Stefan Ericsson,et al.  Towards automatic detection of local bearing defects in rotating machines , 2005 .

[7]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

[8]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals , 2005, Comput. Biol. Medicine.

[9]  Shih-Fu Ling,et al.  Bearing failure detection using matching pursuit , 2002 .

[10]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients , 2005, Journal of Neuroscience Methods.

[11]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[12]  Janko Petrovčič,et al.  An approach to fault diagnosis of vacuum cleaner motors based on sound analysis , 2005 .

[13]  Ship-Peng Lo,et al.  The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process , 2005 .

[14]  V. Purushotham,et al.  Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition , 2005 .

[15]  Fevzullah Temurtas,et al.  A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems , 2006 .

[16]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[17]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[18]  B. Samanta,et al.  Artificial neural networks and genetic algorithms for gear fault detection , 2004 .

[19]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[20]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[21]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[22]  A. Rechester,et al.  Symbolic Analysis of Chaotic Signals and Turbulent Fluctuations , 1997 .