基于多目标交叉熵优化的轮对轴承故障特征提取方法

doi:10.3901/JME.2018.04.285

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

基于多目标交叉熵优化的轮对轴承故障特征提取方法

顾晓辉^1,2, 杨绍普^1,2, 刘永强², 任彬², 张建超²

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:
国家自然科学基金（U1534204，11472179，11572206，51405313，11372197，51375319）和河北省自然科学基金（A2016210099）资助项目

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

GU Xiaohui^1,2, YANG Shaopu^1,2, LIU Yongqiang², REN Bin², ZHANG Jianchao²

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/Abstract

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

中图分类号:

U260
TH17

顾晓辉, 杨绍普, 刘永强, 任彬, 张建超. 基于多目标交叉熵优化的轮对轴承故障特征提取方法[J]. 机械工程学报, 2018, 54(4): 285-292.

GU Xiaohui, YANG Shaopu, LIU Yongqiang, REN Bin, ZHANG Jianchao. Fault Feature Extraction of Wheel-bearing Based on Multi-objective Cross Entropy Optimization[J]. Journal of Mechanical Engineering, 2018, 54(4): 285-292.

参考文献

[1] YI C, LIN J, ZHANG W, et al. Faults diagnostics of railway axle bearings based on IMF's confidence index algorithm for ensemble EMD[J]. Sensors, 2015, 15(5):10991-11011.
[2] CAO H, FAN F, ZHOU K, et al. Wheel-bearing fault diagnosis of trains using empirical wavelet transform[J]. Measurement, 2016, 82:439-449.
[3] 黄采伦, 李忠, 王靖, 等. 基于时延自相关ICA的列车轮对轴承复合故障诊断方法[J]. 铁道学报, 2016, 38(5):36-41. HUANG Cailun, LI Zhong, WANG Jing, et al. Compound fault diagnosis based on time delay autocorrelation ICA for train wheelset bearing[J]. Journal of the China Railway Society, 2016, 38(5):36-41.
[4] CHEN J, LI Z, PAN J, et al. Wavelet transform based on inner product in fault diagnosis of rotating machinery:A review[J]. Mechanical Systems and Signal Processing, 2016, 70:1-35.
[5] YAN R, GAO R X, CHEN X. Wavelets for fault diagnosis of rotary machines:A review with applications[J]. Signal Processing, 2014, 96:1-15.
[6] 梁霖, 徐光华. 基于自适应复平移Morlet小波的轴承包络解调分析方法[J]. 机械工程学报, 2006, 42(10):151-155. LIANG Lin, XU Guanghua. Optimal envelope demodulation method based on adaptive complex shifted Morlet wavelet in bearing diagnosis[J]. Chinese Journal of Mechanical Engineering, 2006, 42(10):151-155.
[7] BOZCHALOOI I S, LIANG M. A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection[J]. Journal of Sound and Vibration, 2007, 308(1):246-267.
[8] BOZCHALOOI I S, LIANG M. A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals[J]. Mechanical Systems and Signal Processing, 2008, 22(4):915-933.
[9] SU W, WANG F, ZHU H, et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement[J]. Mechanical Systems and Signal Processing, 2010, 24(5):1458-1472.
[10] PETER W T, WANG D. The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection:Part 2 of the two related manuscripts that have a joint title as "Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement-Parts 1 and 2"[J]. Mechanical Systems and Signal Processing, 2013, 40(2):520-544.
[11] 张龙, 熊国良, 黄文艺. 复小波共振解调频带优化方法和新指标[J]. 机械工程学报, 2015, 51(3):95-103. ZHANG Long, XIONG Guoliang, HUANG Wenyi. New procedure and index for the parameter optimization of complex wavelet based resonance demodulation[J]. Journal of Mechanical Engineering, 2015, 51(3):95-103.
[12] BORGHESANI P, PENNACCHI P, CHATTERTON S. The relationship between kurtosis-and envelope-based indexes for the diagnostic of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2014, 43(1):25-43.
[13] ANTONI J. The infogram:Entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing, 2016, 74:73-94.
[14] LI C, CABRERA D, DE OLIVEIRA J V, et al. Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram[J]. Mechanical Systems and Signal Processing, 2016, 76:157-173.
[15] 何正嘉, 訾艳阳, 陈雪峰, 等. 内积变换原理与机械故障诊断[J]. 振动工程学报, 2007, 20(5):528-533. HE Zhengjia, ZI Yanyang, CHEN Xuefeng, et al. Transform principle of inner product for fault diagnosis[J]. Journal of Vibration Engineering, 2007, 20(5):528-533.
[16] FENG K, JIANG Z, HE W, et al. Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet[J]. Measurement, 2011, 44(9):1582-1591.
[17] WANG D, SHEN C, PETER W T. A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults[J]. Journal of Sound and Vibration, 2013, 332(25):6871-6890.
[18] WANG D, MIAO Q. Smoothness index-guided Bayesian inference for determining joint posterior probability distributions of anti-symmetric real Laplace wavelet parameters for identification of different bearing faults[J]. Journal of Sound and Vibration, 2015, 345:250-266.
[19] WANG D, TSUI K L, ZHOU Q. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 72:80-91.
[20] RUBINSTEIN R Y, RIDDER A, VAISMAN R. Fast sequential Monte Carlo methods for counting and optimization[M]. New York:John Wiley & Sons, 2013.
[21] BEKKER J, ALDRICH C. The cross-entropy method in multi-objective optimisation:An assessment[J]. European Journal of Operational Research, 2011, 211(1):112-121.
[22] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.

基于多目标交叉熵优化的轮对轴承故障特征提取方法

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王涵, 江志刚, 张华, 王彦. 基于目标级联的废旧机械装备多目标优化再设计方法研究[J]. 机械工程学报, 2019, 55(3): 147-153.
[2]	崔大宾, 赵锐, 李俊, 佘家豪, 李立, 温泽峰. 面向轮缘磨耗的动车组车轮型面多目标优化设计[J]. 机械工程学报, 2019, 55(16): 104-113.
[3]	陈栋, 李世其, 王峻峰, 何旺, 丰意. 并联机构的运动学多目标轨迹规划方法[J]. 机械工程学报, 2019, 55(15): 163-173.
[4]	管超, 张则强, 李云鹏, 贾林. 多行设备布局的一种多目标差分进化算法和线性规划混合方法[J]. 机械工程学报, 2019, 55(13): 160-174.
[5]	熊玮, 黄海鸿, 朱利斌, 刘志峰. 基于产品制造过程内含能分级的冲压车间节能调度研究[J]. 机械工程学报, 2019, 55(10): 217-225.
[6]	俞燎宏, 荣见华, 赵志军, 陈一雄, 李方义. 多工况载荷下连续体结构柔顺度拓扑优化问题的新的求解方法[J]. 机械工程学报, 2018, 54(5): 210-219.
[7]	李连升, 邓楼楼, 梅志武, 吕政欣, 刘继红. 聚焦型X射线脉冲星望远镜Pareto多目标优化与多场耦合分析[J]. 机械工程学报, 2018, 54(23): 174-184.
[8]	杨继斌, 徐晓惠, 张继业, 宋鹏云. 燃料电池有轨电车能量管理策略多目标优化[J]. 机械工程学报, 2018, 54(22): 153-159.
[9]	蒋荣超, 刘大维, 王登峰. 基于熵权TOPSIS方法的整车动力学性能多目标优化[J]. 机械工程学报, 2018, 54(2): 150-158.
[10]	尹超, 赵旭, 邱磊. 网络化协作加工设备约束特性及一种优化选择方法[J]. 机械工程学报, 2018, 54(13): 192-201.
[11]	林腾蛟, 曹洪, 谭自然, 何泽银, 吕和生. 四级行星齿轮减速器耦合系统动态性能优化[J]. 机械工程学报, 2018, 54(11): 161-171.
[12]	陈波, 高殿荣, 杨超, 毋少峰, 张光通. 大功率远程射雾器结构参数多目标智能协同优化^*[J]. 机械工程学报, 2017, 53(6): 166-175.
[13]	薛臣, 江志刚, 张旭刚, 王涵. 废旧机械装备零部件重用组合多目标优化模型及应用^*[J]. 机械工程学报, 2017, 53(5): 76-85.
[14]	张亮, 张继业, 李田, 张亚东. 超高速列车流线型头型多目标优化设计^*[J]. 机械工程学报, 2017, 53(2): 106-114.
[15]	辜小花, 王坎, 李燕, 高论, 李太福, 周伟. 基于演化模型偏好多目标优化的智能采油辅助决策支持[J]. 机械工程学报, 2017, 53(13): 159-169.