Composite fault diagnosis method of rolling bearing based on consistent optimization index

被引：0
|
作者：
Zhang L. [1 ]
Cai B. [1 ]
Xiong G. [1 ]
Hu J. [2 ]
机构：
[1] School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang
[2] Institute of Science and Technology, Nanchang Railway Bureau, Nanchang
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2021年 / 40卷 / 09期
关键词：
Correlated kurtosis; Fault diagnosis; Feature enhancement; Wavelet transform;
D O I：
10.13465/j.cnki.jvs.2021.09.031
中图分类号：
学科分类号：
摘要：
Cycle impact caused by rolling bearing local fault transmitted from bearing to sensor is affected by transmission path, environmental noise and accidental impact interference, which makes fault feature extraction difficult and diagnosis effect poor. Here, a composite fault diagnosis method of rolling bearing based on the maximum correlation kurtosis deconvolution and adjustable quality factor wavelet transform was proposed. The former was used to weaken the influence of transfer path, while the latter was used to suppress noise components with filtering, and their parameter optimizations consistently took the correlated kurtosis, which could consider characteristics of rolling bearing fault impact cycle, as the optimization index to ensure the overall effect of feature extraction. Meanwhile, this index could not be affected by accidental impact interference. Simulated and test signals were analyzed using the proposed method, and the results were compared to those using the common methods, such as, fast spectral kurtosis to verify the effectiveness and superiority of the proposed method in rolling bearing fault diagnosis. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：237 / 245
页数：8
相关论文
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