Rolling Bearing Performance Degradation Assessment Based on FOA-WSVDD

被引：2
|
作者：
Zhu S. [1 ]
Bai R. [1 ]
Liu Q. [1 ]
机构：
[1] Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi, 214122, Jiangsu
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2018年 / 29卷 / 05期
关键词：
Bearing; Fruit fly optimization algorithm(FOA); Wave support vector data description(WSVDD); Wavelet kernel;
D O I：
10.3969/j.issn.1004-132X.2018.05.016
中图分类号：
学科分类号：
摘要：
A rolling bearing performance degradation assessment method was proposed based on FOA-WSVDD, aiming at the problems that the SVDD algorithm was not sensitive to rolling bearing early faults and difficult to select suitable kernel parameters. The feature vectors of the time and time frequency domains were extracted from bearing fault-free stages and then were selected based on monotonicity. Then, the FOA-WSVDD model was established where the wavelet kernel function was introduced to overcome the problems that the existing kernel function was not sensitive to the early faults of the rolling bearings, and kernel parameters were optimized based on the improved FOA where the ratio of the numbers of support vectors and the total samples was used as fitness function. Finally, feature vectors were input into the WSVDD model, and the bearing performance degradation index was obtained. The experimental results show that the proposed method may accurately predict the bearing early faults, and it is 17 hours earlier than that of the SVDD algorithm which is based on Gauss kernel function. © 2018, China Mechanical Engineering Magazine Office. All right reserved.
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页码：602 / 608
页数：6
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