Application of an improved blind deconvolution algorithm to acoustic-based rolling bearing defect detection

Yu Wang;Yilin Chi;Xing Wu;Xiongwei Guo

Journal of Vibration and Shock ›› 2010, Vol. 29 ›› Issue (6) : 11-14.

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PDF(2255 KB)
Journal of Vibration and Shock ›› 2010, Vol. 29 ›› Issue (6) : 11-14.
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Application of an improved blind deconvolution algorithm to acoustic-based rolling bearing defect detection

  • Yu Wang; Yilin Chi; Xing Wu; Xiongwei Guo
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Abstract

An improved time-domain blind deconvolution algorithm was proposed, based on genetic algorithm (GA) and higher order statistics (HOS). A newly defined distance measure based on kurtosis was employed to improve the classification accuracy of independent components in the cluster analysis process, and a GA was applied to search for an optimal length of blind deconvolution filters. With the help of these enhancements, this improved algorithm leads to perfect convolutive source separation for acoustic-based machine diagnosis. Both numerical and experimental studies were carried out. The results show that this algorithm can efficiently extract acoustic signals of fault bearings in real-world situations.

Key words

Blind deconvolution / Rolling element Bearing / Acoustic-based machine diagnosis / Cluster analysis / Independent component analysis

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Yu Wang;Yilin Chi;Xing Wu;Xiongwei Guo. Application of an improved blind deconvolution algorithm to acoustic-based rolling bearing defect detection[J]. Journal of Vibration and Shock, 2010, 29(6): 11-14
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