基于自适应紧框架学习的轴承故障诊断

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Abstract A bearing fault diagnosis method based on adaptive compact frame learning was proposed to solve the problem that by the method of traditional multi-scale transformation, it is difficult to do the diagnosis adaptively.In view of the obvious correlation between the atoms of an over-complete dictionary, which affects the reconstruction effect of the fault signal, a compact framework in the condition of noise was constructed by learning.The method, could not only effectively describe the bearing fault signal adaptively, but also completely reconstruct the fault characteristic signal.Considering each fault mode having different characteristic frequencies, by making use of the tight frame, different frequency responses were generated by different filters, and according to the fault characteristic frequencies,fault features were constructed.A support vector machine(SVM) based on genetic algorithm optimization was used for classification training and testing.The experimental results show that the proposed algorithm is effective in fault classification of bearings and has good diagnostic capability for different faults.

Key words： fault diagnosis tight framework genetic algorithm support vector machine(SVM)

Received: 04 December 2019 Published: 28 May 2021

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	Articles by authors
	BAI Zhuangzhuang
	LU Yixiang
	GAO Qingwei
	SUN Dong

Cite this article:

BAI Zhuangzhuang,LU Yixiang,GAO Qingwei, et al. Bearing fault diagnosis based on adaptive tight frame learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(10): 296-303.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I10/296

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