Underdetermined blind extraction for bearings combined failures based on the improved frequency domain compression sense

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (14) : 123-128.

ZHOU Jun，WU Xing，CHI Yi-lin，PAN Nan

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Abstract

Aiming at the lack of frequency-domain blind extraction algorithm for complex rotating machinery fault, and improving the separation results accuracy of underdetermined blind extraction, a method based on multi-scale multi- structure close-open average combination morphological filtering(C-OACMF), genetic simulated annealing clustering (GASA) and frequency domain compressive sensing algorithm(CS) was proposed to deal with underdetermined blind fault feature extraction . First, the C-OACMF was used to filter out background noise and extract the shock signals; then, using the GASA of fuzzy C-average clustering algorithm to estimate the mixing matrix; finally the sensor matrix was remodeled by the estimation matrix and the orthogonal matching pursuit of frequency domain CS algorithm was used to estimate the source signals .The results of computer simulation and real rolling bearing signals analysis show that this proposed method is quite effective.

Key words

improved morphological filtering / genetic simulated annealing clustering / frequency domain compression sense / bearing fault / underdetermined blind extraction

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ZHOU Jun，WU Xing，CHI Yi-lin，PAN Nan . Underdetermined blind extraction for bearings combined failures based on the improved frequency domain compression sense[J]. Journal of Vibration and Shock, 2015, 34(14): 123-128

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