Fault feature extraction for rolling bearings&nbsp; based on generalized S transformation optimized with Quantum genetic algorithm

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (5) : 108-113.

Fault feature extraction for rolling bearings based on generalized S transformation optimized with Quantum genetic algorithm

WANG Bo1,LIU Shulin2,ZHANG Hongli2

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Abstract

Considering noise’s effects on fault feature extraction of rolling bearings in practical engineering environment,a novel method for rolling bearing fault feature extraction based on the generalized S transformation optimized with the quantum genetic algorithm (QGA) was proposed.Firstly,the optimal window control parameters of the generalized S transformation were selected adaptively with QGA taking the concentration the level of time-frequency distribution as the evaluation standard.Then the mode vectors of the complex time-frequency matrix formed after fault vibration signals of rolling bearings were transformed with the generalized S transformation were extracted as rolling bearing fault feature vectors.The method was applied to extract rolling bearing fault feature and compared with other methods using fault diagnosis tests.The results showed that the proposed method can extract fault features more accurately than other methods can.Moreover,the fault feature extraction tests of rolling bearing vibration signals under different levels of background noise indicated that the proposed method has a good anti-noise capability and a strong robustness.

Key words

generalized S transform / quantum genetic algorithm / rolling bearing / fault diagnosis / feature extraction

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WANG Bo1,LIU Shulin2,ZHANG Hongli2. Fault feature extraction for rolling bearings based on generalized S transformation optimized with Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2017, 36(5): 108-113

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