Research on Fault Diagnosis Method of Rolling Bearings Based on Cuckoo Search Algorithm and Maximum Second Order Cyclostationary Blind Deconvolution

doi:10.3901/JME.2021.09.099

Abstract

Abstract: Aiming at the problem that the effectiveness of bearing fault diagnosis for maximum second order cyclostationary blind deconvolution (CYCBD) depends on the accuracy of the selected fault feature frequency and the length of the filter, we propose a diagnosis method that uses cuckoo search algorithm (CSA) to optimize CYCBD and uses the improved maximum harmonic significance index (IHSI) as the optimization basis. Firstly, The search range of fault feature frequency and filter length is estimated. then, CSA was used to compare the IHSI values of the deconconvolution signals at different fault feature frequencies and filter lengths, and the fault feature frequencies and filter lengths corresponding to the maximum IHSI values were selected as the input parameters of CYCBD. Finally, The square envelope of the deconvolve signal is used to extract fault features. Simulation and experiment show that CSA can efficiently find the accurate fault characteristic frequency and the appropriate filter length to ensure the deconvolution effect of CYCBD, and the comparisons of CYCBD with minimum entropy deconvolution (MED) and maximum correlation kurtosis deconvolution (MCKD) show that CYCBD has stronger fault feature extraction capability.

Key words: rolling element bearing, fault diagnosis, cuckoo search algorithm, maximum second order cyclostationary blind deconvolution

CLC Number:

TG156

HUANG Baoyu, ZAHNG Yongxiang, ZHAO Lei. Research on Fault Diagnosis Method of Rolling Bearings Based on Cuckoo Search Algorithm and Maximum Second Order Cyclostationary Blind Deconvolution[J]. Journal of Mechanical Engineering, 2021, 57(9): 99-107.

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