基于自适应加权多尺度组合形态滤波的轴承故障特征提取研究

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Abstract Aiming at the problem of vibration signals of rolling bearings being often accompanied by noise interference during early fault diagnosis, a selective adaptive weighted multi-scale combination morphological filtering (AWMCMF) method was proposed to extract fault features from vibration signals. Firstly, 3 types of combined operators were used to form a group of new morphological operators being able to effectively extract positive and negative impact features in vibration signals. Secondly, based on new operators, a weighted multi-scale morphological filtering method was proposed, and Teager energy kurtosis was taken as the evaluation index to provide optimized weights for various scales. Finally, the weighted binding was performed for selective adaptive weights and multi-scale operators to obtain the optimized fault feature extraction results. The results of simulated signals and bearing faulty vibration signals showed that the proposed method can be used to effectively filter noise and extract fault features.

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Key words： rolling bearings fault diagnosis vibration signal impact feature multi-scale morphological filtering

Received: 03 May 2018 Published: 28 December 2019

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	Articles by authors
	HAN Xiaole
	HU Tianzhong
	YU Jianbo

Cite this article:

HAN Xiaole,HU Tianzhong,YU Jianbo. Feature extraction of bearing faults based on adaptive weighted multi-scale combination morphological filtering[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(1): 245-252.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I1/245

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