• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2018, Vol. 54 ›› Issue (4): 285-292.doi: 10.3901/JME.2018.04.285

• 故障诊断 • 上一篇    


顾晓辉1,2, 杨绍普1,2, 刘永强2, 任彬2, 张建超2   

  1. 1. 石家庄铁道大学交通运输学院 石家庄 050043;
    2. 河北省交通工程结构力学行为演变与控制重点实验室 石家庄 050043
  • 收稿日期:2017-05-02 修回日期:2017-12-25 发布日期:2018-02-20
  • 通讯作者: 杨绍普(通信作者),男,1962年出生,博士,教授,博士研究生导师。主要研究方向为机械系统动力学与控制。E-mail:yangsp@stdu.edu.cn
  • 作者简介:顾晓辉,男,1988年出生,博士研究生。主要研究方向为机械系统信号处理与故障诊断。E-mail:guxh1988@126.com
  • 基金资助:

Fault Feature Extraction of Wheel-bearing Based on Multi-objective Cross Entropy Optimization

GU Xiaohui1,2, YANG Shaopu1,2, LIU Yongqiang2, REN Bin2, ZHANG Jianchao2   

  1. 1. School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043;
    2. Key Laboratory of Mechanical Evolution and Control of Traffic Structure in Hebei, Shijiazhuang 050043
  • Received:2017-05-02 Revised:2017-12-25 Published:2018-02-20

摘要: 从复杂干扰中提取轮对轴承的故障特征,需要同时从冲击性和循环平稳性两个方面考虑。而在提取过程中,单一的指标很难统一并平衡二者的权重。为此,提出一种基于多目标优化的非对称实Laplace小波解调方法。在第一个目标中,以最大化平方包络的峭度为适应度函数,利用窄带信号包络的时域稀疏性表征故障的冲击性特征。在第二个目标中,以最大化平方包络谱的峭度为适应度函数,利用窄带信号包络的频域稀疏性表征故障的循环平稳性特征。并通过将非支配排序和拥挤距离排序引入交叉熵算法实现了多目标最优非对称实Laplace小波解调以降低冲击性噪声或循环平稳性噪声的干扰。试验结果表明,该方法可实现复杂干扰下轮对轴承的故障特征提取,并通过与单目标优化方法的对比分析,验证了该方法的优越性。

关键词: 多目标优化, 非对称实Laplace小波, 故障特征提取, 交叉熵, 轮对轴承

Abstract: It is crucial to take impulsiveness and cyclostationarity into account simultaneously in the fault features extraction of wheel-bearing, especially with the occurrence of complex interferences from wheel-rail contact relation. However, these two aspects can hardly be synthesized and balanced by one index. Therefore, a novel multi-objective optimized anti-symmetric real Laplace wavelet filtering method is proposed to deal with this problem. The first fitness function is designed by maximizing the kurtosis value of squared envelope, which is representing the impulsiveness. And, the second fitness function is designed by maximizing the kurtosis value of squared envelope spectrum, which is representing the cyclostationarity. Through combining the non-dominated sorting and crowded comparison, the parameters of the wavelet filter are optimized by the improved cross entropy algorithm, which is immune to impulsive or cyclostationary noises. One vibration signal from a faulty wheel-bearing is investigated to illustrate the effectiveness of the proposed method, and some comparisons with the single-objective method are also conducted to show its superiority in extracting the repetitive transients.

Key words: anti-symmetric real Laplace wavelet, cross entropy, fault feature extraction, multi-objective optimization, wheel-bearing