A two-step blind source extraction method and its application in fault diagnosis of rolling element bearing

The vibration signals will take on cyclical characteristics when fault arises in the rolling element bearing, and a two-step blind source extraction (BSE) method for fault diagnosis of rolling element bearing is proposed in the paper using the above property. Firstly, calculate the theoretical basic cyclet of the target source fault signal, and the weighted separation matrix ŵ and desired source signal are obtained coarsely. Secondly, use ŵ as the initial weighted matrix and apply the fixed-point algorithm basing on high-order statistics on the observed signals, and much more perfect target source signal is got at last. The proposed method has the following advantages over other BSE method such as constrained independent component analysis (CICA) basing on the analyzed results of simulation and experiment: The fundamental period τ of the target source signal does not needed to be estimated accurately, and the reference signal also does not need to be constructed precisely. However, these two conditions are the necessary prerequisites of CICA. Besides, the proposed method also has the advantage of higher accuracy over the other recent BSE methods through comparison.

[1]  Ning Jiang,et al.  Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications , 2017, Front. Neurosci..

[2]  Allan Kardec Barros,et al.  Extraction of Specific Signals with Temporal Structure , 2001, Neural Computation.

[3]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[4]  Driss Aboutajdine,et al.  A frequency domain-based approach for blind MIMO system identification using second-order cyclic statistics , 2009, Signal Process..

[5]  Bangyan Zhou,et al.  A permutation algorithm based on dynamic time warping in speech frequency-domain blind source separation , 2017, Speech Commun..

[6]  Sergio Bermejo,et al.  Finite sample effects in higher order statistics contrast functions for sequential blind source separation , 2005, IEEE Signal Processing Letters.

[7]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[8]  Zhang Yi,et al.  Robust extraction of specific signals with temporal structure , 2006, Neurocomputing.

[9]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[10]  J. Antoni.  Blind separation of vibration components: Principles and demonstrations , 2005 .

[11]  Zhi-Lin Zhang,et al.  Morphologically constrained ICA for extracting weak temporally correlated signals , 2008, Neurocomputing.

[12]  Wei Lu,et al.  Constrained Independent Component Analysis , 2000, NIPS.

[13]  Yu Zhou,et al.  Constrained independent component analysis and its application to machine fault diagnosis , 2011 .

[14]  Zan Li,et al.  Time differences of arrival estimation of mixed interference signals using blind source separation based on wireless sensor networks , 2016, IET Signal Process..

[15]  Hyung-Min Park,et al.  Efficient online target speech extraction using DOA-constrained independent component analysis of stereo data for robust speech recognition , 2015, Signal Process..

[16]  Naotaka Fujii,et al.  A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal , 2017, Neural Networks.