Blind Deconvolution and Frequency Domain Compressive Sensing Application in Bearing Composite Acoustic Fault Diagnosis

doi:10.3901/JME.2016.03.063

Abstract

Abstract: According to the problem of time-domain blind deconvolution algorithm is active only for a single fault mechanical sound signal, and the traditional sparse component analysis is failure to the acoustic signal analysis, a method based on blind deconvolution, morphological filtering and frequency domain compressed sensing reconstruction of sparse component analysis is proposed to deal with the composite acoustics bearing fault diagnosis. The time-domain blind deconvolution algorithm is used to prefer solution components result as well as to extract the impact component of the acoustic signal. Background noise is filtered out by using the morphological filtering. By using fuzzy C-means clustering estimated mixing matrix, the sensor matrix is remodeled based on mixing matrix, and the sparsity adaptive matching pursuit based algorithm of frequency-domain compressed sensing algorithm is used to reconstruct the separated signals. Dual real rolling bearing fault acoustic signal analysis results show that this method can effectively separate and extract the rolling bearing fault characteristics.

Key words: acoustic diagnosis, bearing composite fault, blind deconvolution, frequency domain compression sense, morphological filtering

CLC Number:

TH165
TP206

ZHOU Jun, WU Xing, CHI Yilin, PAN Nan, LIU Chang. Blind Deconvolution and Frequency Domain Compressive Sensing Application in Bearing Composite Acoustic Fault Diagnosis[J]. Journal of Mechanical Engineering, 2016, 52(3): 63-70.

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