基于鲸鱼算法优化WKELM的滚动轴承故障诊断

doi:10.16182/j.issn1004731x.joss.201709042

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (9): 2189-2197.doi: 10.16182/j.issn1004731x.joss.201709042

基于鲸鱼算法优化WKELM的滚动轴承故障诊断

徐继亚, 王艳, 纪志成

江南大学物联网技术应用教育部工程研究中心,无锡 214122

收稿日期:2017-05-18 发布日期:2020-06-02
作者简介:徐继亚(1993-), 男, 山东枣庄, 硕士生, 研究方向为智能故障诊断与预测控制; 王艳(1978-),女,江苏盐城,博士后,教授,研究方向为网络化控制系统,制造系统能效优化。
基金资助:
国家自然科学基金(61572238),江苏省杰出青年基金(BK20160001)

Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm

Xu Jiya, Wang Yan, Ji Zhicheng

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China

Received:2017-05-18 Published:2020-06-02

摘要/Abstract

摘要： 为了准确有效提取滚动轴承振动信号中最优的故障信息,判断出滚动轴承故障的类型,提出了一种基于正交匹配追踪算法和优化小波核极限学习机的滚动轴承故障诊断方法。运用正交匹配追踪算法对轴承振动信号进行降噪处理,对去噪后的信号进行小波包分解求取频带能量提取故障特征。采用基于冯诺依曼拓扑结构鲸鱼算法(Whale Optimization Algorithm, WOA)来优化WKELM的惩罚因子和核函数的参数,构造滚动轴承故障分类器模型。实验结果表明,该方法能有效提取滚动轴承故障特征信息,具有较高的诊断精度。

关键词: 正交匹配追踪, 小波核极限学习机, 鲸鱼算法, 滚动轴承

Abstract: In order to recognize rolling bearing's fault types accurately according to the optimal characteristics of fault vibration signal of rolling bearing, a rolling bearing fault diagnosis method was proposed based on orthogonal matching pursuit algorithm and the optimized wavelet kernel extreme learning machine method. The OMP algorithm was used to de-noising the vibration signal of the bearing. The wavelet packet decomposition of the signal after de-noising was used to obtain the frequency band energy, and the fault characteristics were extracted. By using an improved whale optimization algorithm based on von-neumann, the penalty factor and kernel parameter of wavelet kernel extreme learning machine were optimized to design a classifier of rolling bearing's fault types. The experimental results prove that the proposed method can accurately and effectively identify the fault type.

Key words: orthogonal matching pursuit, wavelet kernel extreme learning machine, WOA, rolling bearing

中图分类号:

TP391.9

徐继亚, 王艳, 纪志成. 基于鲸鱼算法优化WKELM的滚动轴承故障诊断[J]. 系统仿真学报, 2017, 29(9): 2189-2197.

Xu Jiya, Wang Yan, Ji Zhicheng. Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm[J]. Journal of System Simulation, 2017, 29(9): 2189-2197.

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基于鲸鱼算法优化WKELM的滚动轴承故障诊断

Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm

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