An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by time–frequency distribution image dimensionality reduction. Since time–frequency distribution image can reveal intrinsic feature of nonstationary signals and can make the weak impulses feature prominent, and besides, the transient impulse feature and the noise component lie in different dimensions, so using the dimensionality reduction method based on singular value decomposition to suppress the background noise in the raw time–frequency distribution image is motivated. A bearing outer race fault signal obtained from a test-to-failure experiment and a bearing inner race fault signal obtained from an experimental motor are employed to demonstrate the enhanced performance of the proposed method in faint signal extraction. The results indicate that the proposed method outperforms the fast kurtogram method and is effective in faint signal extraction.

[1]  Shaobo Han,et al.  Time domain averaging based on fractional delay filter , 2009 .

[2]  Peter W. Tse,et al.  Detection of the rubbing-caused impacts for rotor–stator fault diagnosis using reassigned scalogram , 2005 .

[3]  Fanrang Kong,et al.  Detection of signal transients using independent component analysis and its application in gearbox condition monitoring , 2007 .

[4]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[5]  Ruqiang Yan,et al.  Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines , 2012 .

[6]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .

[7]  Paul R. White,et al.  THE ANALYSIS OF NON-STATIONARY SIGNALS USING TIME-FREQUENCY METHODS , 1996 .

[8]  Yaguo Lei,et al.  Fast-varying AM-FM components extraction based on an adaptive STFT , 2012, Digit. Signal Process..

[9]  Zhipeng Feng,et al.  Application of atomic decomposition to gear damage detection , 2007 .

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

[11]  Zhongkui Zhu,et al.  Detection of signal transients based on wavelet and statistics for machine fault diagnosis , 2009 .

[12]  Ruqiang Yan,et al.  Harmonic wavelet-based data filtering for enhanced machine defect identification , 2010 .

[13]  José María Carazo,et al.  Fringe pattern denoising by image dimensionality reduction , 2013 .

[14]  Guanghua Xu,et al.  Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis , 2015 .

[15]  Changqing Shen,et al.  A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis , 2013 .

[16]  A Al-Zaben,et al.  Extraction of foetal ECG by combination of singular value decomposition and neuro-fuzzy inference system , 2006, Physics in medicine and biology.

[17]  P. D. McFadden,et al.  A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox , 1991 .

[18]  Jin Chen,et al.  Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis , 2013 .

[19]  Fanrang Kong,et al.  Machine fault signature analysis by midpoint-based empirical mode decomposition , 2011 .

[20]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[21]  Fulei Chu,et al.  Time–frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation , 2011 .

[22]  Yaguo Lei,et al.  An enhanced stochastic resonance method for weak feature extraction from vibration signals in bearing fault detection , 2014 .

[23]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[24]  Weiguo Huang,et al.  The elimination of cross-terms based on the fusion of time-frequency features and its application in machine fault diagnosis , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.