Fault detection for rolling element bearing based on repeated single-scale morphology and simplified sensitive factor algorithm

Abstract A hybrid of repeated single-scale morphological filtering (RSSMF) and simplified sensitive factor (SSF) method is proposed to detect the fault signals of rolling element bearing. First, unit scale (three sampling points) in the morphology filtering is introduced to retain more feature components of a signal. To obtain a satisfied effect in morphological filtering, a repeated morphological differential operator (RMDO) is developed to perform in the RSSMF. After the repeated morphological filtering is implemented, a series of outputs are achieved. Some of them comprise interested information and others contain irrelevant one. To highlight useful information, some factors that are sensitive to the useful information are computed by the simplified sensitive factor algorithm. Finally, the reconstructed signals are obtained by the weighting sensitive factors. The proposed method is assessed by both simulation analysis and vibration signals of the rolling element bearings with the outer and inner race faults. Compared with traditional single-scale morphological filtering (TSSMT) and traditional multi-scale morphological filtering (TMSMT), the results demonstrate that the proposed approach has superior performance in noise removal and fault feature detection.

[1]  Dejie Yu,et al.  Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings , 2005 .

[2]  Min-Chun Pan,et al.  An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis , 2012 .

[3]  Zhengjia He,et al.  Wheel-bearing fault diagnosis of trains using empirical wavelet transform , 2016 .

[4]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

[5]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[6]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[7]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[8]  Miguel A. Ferrer,et al.  Application of the Teager-Kaiser energy operator in bearing fault diagnosis. , 2013, ISA transactions.

[9]  Yaguo Lei,et al.  Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs , 2009 .

[10]  Peter W. Tse,et al.  A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals , 2013 .

[11]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[12]  Yanbin Yuan,et al.  An efficient chaos embedded hybrid approach for hydro-thermal unit commitment problem , 2015 .

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

[14]  Xiaohui Yuan,et al.  An improved artificial physical optimization algorithm for dynamic dispatch of generators with valve-point effects and wind power , 2014 .

[15]  Zhixiong Li,et al.  A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator , 2016 .

[16]  Nagarajan Murali,et al.  Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference , 2013, IEEE Transactions on Industrial Electronics.

[17]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

[18]  Xiaohui Yuan,et al.  Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II , 2014 .

[19]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[20]  Xiaohui Yuan,et al.  Nonlinear dynamic analysis and robust controller design for Francis hydraulic turbine regulating system with a straight-tube surge tank , 2017 .

[21]  Jérôme Antoni,et al.  Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment , 2017 .

[22]  Yang Li,et al.  Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis , 2015 .

[23]  Bing Li,et al.  Gear fault detection using multi-scale morphological filters , 2011 .

[24]  Fulei Chu,et al.  Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings , 2013 .

[25]  Xiaohui Yuan,et al.  Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration , 2014 .

[26]  I. R. Praveen Krishna,et al.  Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings , 2012 .

[27]  Erik Leandro Bonaldi,et al.  Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current , 2015, IEEE Transactions on Industrial Electronics.

[28]  Yanbin Yuan,et al.  An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost , 2015 .

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

[30]  Abdolreza Ohadi,et al.  Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients , 2011, Appl. Soft Comput..