基于ELMD和灰色相似关联度的滚动轴承故障诊断研究
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摘要
针对滚动轴承发生故障时,其故障信号不同频带的能量分布变化与其故障状态之间存在一定关系的情况,提出了一种总体局部均值分解(Ensemble Local Mean Decomposition, ELMD)与灰色相似关联度相结合的滚动轴承故障诊断方法.首先,选取滚动轴承处于不同故障状态的样本信号,利用ELMD对其进行分解并得到若干乘积函数(Product Function, PF);然后,计算每个PF分量的能量分布并构造特征向量;最后,结合灰色相似关联度对滚动轴承故障状态进行分析和识别,并与LMD(Local Mean Decomposition, LMD)和EEMD(Ensemble Empirical Mode Decomposition)方法进行对比,其实验对比分析结果论证了方法的可行性,也为滚动轴承的故障诊断提供了新的解决方案.
Aiming at the certain relationship between energy distribution changes in different frequency bands of fault signals and fault state when failures happen in the rolling bearings, a fault diagnosis method based on ensemble local mean decomposition(ELMD) and grey similarity degree is proposed. First, the sample signals of rolling bearing in different fault states were selected and decomposed into several product functions(PF) through ELMD; then, the energy distribution of each PF component was calculated and feature vectors were constructed; finally, grey similarity degree was combined with ELMD to realize the analysis and recognition of fault status, and ELMD was compared with local mean decomposition(LMD) and ensemble empirical mode decomposition(EEMD). The experimental results show the feasibility of the method and provide a new solution for fault diagnosis of rolling bearings.
Aiming at the certain relationship between energy distribution changes in different frequency bands of fault signals and fault state when failures happen in the rolling bearings, a fault diagnosis method based on ensemble local mean decomposition(ELMD) and grey similarity degree is proposed. First, the sample signals of rolling bearing in different fault states were selected and decomposed into several product functions(PF) through ELMD; then, the energy distribution of each PF component was calculated and feature vectors were constructed; finally, grey similarity degree was combined with ELMD to realize the analysis and recognition of fault status, and ELMD was compared with local mean decomposition(LMD) and ensemble empirical mode decomposition(EEMD). The experimental results show the feasibility of the method and provide a new solution for fault diagnosis of rolling bearings.
引文
[1] 何正嘉, 袁静, 訾艳阳. 机械故障诊断的内积变换原理与应用[M]. 北京:科学出版社, 2012.
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[6] 马潇, 朱晓军. 基于小波包的LMD在EEG信号分析中的研究[J]. 计算机科学, 2016, 43(6):112-115.
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[11] 张文斌, 苏艳萍, 普亚松, 等. 基于集合经验模式分解能量分布与灰色相似关联度的齿轮故障诊断[J]. 机械工程学报, 2014, 50(7):70-77.
[12] 裴峻峰, 于志远, 董雪, 等. 基于LMD和灰色相似关联度的轴承故障诊断方法[J]. 机械设计与制造, 2015(4):66-69.
[13] Zhaohua Wu, Norden E Huang. Ensemble Empirical Mode Decomposition:A Noise-assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2005, 1(1):1-41.
[14] 张岳. 多传感器信息融合技术在电机故障诊断中的应用研究[D]. 兰州:兰州理工大学, 2014.
[15] 卢绪祥,苏一呜,吴家腾,等.基于EMD及灰色关联度的滑动轴承润滑状态故障诊断研究[J].动力工程学报, 2016,36(1):42-47.
[2] Smith J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2(5):443-454.
[3] 程军圣, 史美丽, 杨宇. 基于LMD与神经网络的滚动轴承故障诊断方法[J]. 振动与冲击, 2010, 29(8):141-144.
[4] 张淑清, 孙国秀, 李亮, 等. 基于LMD近似熵和FCM聚类的机械故障诊断研究[J]. 仪器仪表学报, 2013, 34(3):714-720.
[5] 朱晓军, 樊刘娟, 吕士钦, 等. LMD方法在脑电信号处理中的应用研究[J]. 计算机科学, 2012, 39(2):273-275.
[6] 马潇, 朱晓军. 基于小波包的LMD在EEG信号分析中的研究[J]. 计算机科学, 2016, 43(6):112-115.
[7] 程军圣, 张亢, 杨宇. 基于噪声辅助分析的总体局部均值分解方法[J]. 机械工程学报, 2011, 47(3):55-62.
[8] 崔立志, 刘思峰, 李致平, 等. 一种新的灰色相似关联度模型及其应用[J]. 统计与决策, 2010(7):7-9.
[9] 崔杰. 一种新的灰色相似关联度及其应用[J]. 统计与决策, 2008(20):14-16.
[10] 谭学瑞, 邓聚龙. 灰色关联分析:多因素统计分析新方法[J]. 统计研究, 1995, 12(3):46-48.
[11] 张文斌, 苏艳萍, 普亚松, 等. 基于集合经验模式分解能量分布与灰色相似关联度的齿轮故障诊断[J]. 机械工程学报, 2014, 50(7):70-77.
[12] 裴峻峰, 于志远, 董雪, 等. 基于LMD和灰色相似关联度的轴承故障诊断方法[J]. 机械设计与制造, 2015(4):66-69.
[13] Zhaohua Wu, Norden E Huang. Ensemble Empirical Mode Decomposition:A Noise-assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2005, 1(1):1-41.
[14] 张岳. 多传感器信息融合技术在电机故障诊断中的应用研究[D]. 兰州:兰州理工大学, 2014.
[15] 卢绪祥,苏一呜,吴家腾,等.基于EMD及灰色关联度的滑动轴承润滑状态故障诊断研究[J].动力工程学报, 2016,36(1):42-47.