Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference

Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with morphological operators and fuzzy inference is proposed in this paper. The effectiveness of the morphological operators lies with the correct selection of structuring elements (SEs). This paper also proposes a new algorithm for this SE selection based on kurtosis, thereby making the analysis free of empirical methods. When analyzed with three different sets of faults, the results show that this method is effective and robust in bringing out the impulses. With fuzzy inference being coupled to this new technique, it makes the algorithm to be able to detect early faults also.

[1]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

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

[3]  Mario Pacas,et al.  Frequency Response Analysis for Rolling-Bearing Damage Diagnosis , 2008, IEEE Transactions on Industrial Electronics.

[4]  Jing Wang,et al.  Application of improved morphological filter to the extraction of impulsive attenuation signals , 2009 .

[5]  Jose A. Antonino-Daviu,et al.  Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current , 2011, IEEE Transactions on Industrial Electronics.

[6]  E.L. Owen,et al.  Assessment of the Reliability of Motors in Utility Applications - Updated , 1986, IEEE Transactions on Energy Conversion.

[7]  Ioannis Antoniadis,et al.  APPLICATION OF MORPHOLOGICAL OPERATORS AS ENVELOPE EXTRACTORS FOR IMPULSIVE-TYPE PERIODIC SIGNALS , 2003 .

[8]  Ming J. Zuo,et al.  Mechanical Fault Detection Based on the Wavelet De-Noising Technique , 2004 .

[9]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[10]  Xiao Long Zhang,et al.  Faults diagnosis of rolling element bearings based on modified morphological method , 2011 .

[11]  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.

[12]  Petros Maragos,et al.  Morphological filters-Part I: Their set-theoretic analysis and relations to linear shift-invariant filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[13]  Lijun Zhang,et al.  Multiscale morphology analysis and its application to fault diagnosis , 2008 .

[14]  Seungdeog Choi,et al.  Performance-Oriented Electric Motors Diagnostics in Modern Energy Conversion Systems , 2012, IEEE Transactions on Industrial Electronics.

[15]  Arturo Garcia-Perez,et al.  Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.