Fault Feature Extraction of Wheel-bearing Based on Multi-objective Cross Entropy Optimization

doi:10.3901/JME.2018.04.285

Abstract

Abstract: It is crucial to take impulsiveness and cyclostationarity into account simultaneously in the fault features extraction of wheel-bearing, especially with the occurrence of complex interferences from wheel-rail contact relation. However, these two aspects can hardly be synthesized and balanced by one index. Therefore, a novel multi-objective optimized anti-symmetric real Laplace wavelet filtering method is proposed to deal with this problem. The first fitness function is designed by maximizing the kurtosis value of squared envelope, which is representing the impulsiveness. And, the second fitness function is designed by maximizing the kurtosis value of squared envelope spectrum, which is representing the cyclostationarity. Through combining the non-dominated sorting and crowded comparison, the parameters of the wavelet filter are optimized by the improved cross entropy algorithm, which is immune to impulsive or cyclostationary noises. One vibration signal from a faulty wheel-bearing is investigated to illustrate the effectiveness of the proposed method, and some comparisons with the single-objective method are also conducted to show its superiority in extracting the repetitive transients.

Key words: anti-symmetric real Laplace wavelet, cross entropy, fault feature extraction, multi-objective optimization, wheel-bearing

CLC Number:

U260
TH17

GU Xiaohui, YANG Shaopu, LIU Yongqiang, REN Bin, ZHANG Jianchao. Fault Feature Extraction of Wheel-bearing Based on Multi-objective Cross Entropy Optimization[J]. Journal of Mechanical Engineering, 2018, 54(4): 285-292.

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