基于条件局部均值分解与变量预测模型的轴承故障诊断方法

doi:10.11772/j.issn.1001-9081.2015.09.2606

计算机应用 ›› 2015, Vol. 35 ›› Issue (9): 2606-2610.DOI: 10.11772/j.issn.1001-9081.2015.09.2606

基于条件局部均值分解与变量预测模型的轴承故障诊断方法

许有才, 万舟

昆明理工大学信息工程与自动化学院, 昆明 650500

收稿日期:2015-04-03 修回日期:2015-06-04 出版日期:2015-09-10 发布日期:2015-09-17
通讯作者: 万舟(1960-),男,云南昆明人,副教授,硕士,主要研究方向:PVDF传感材料、机械故障诊断,ynkgwz@aliysun.com
作者简介:许有才(1988-),男,湖南新邵人,硕士研究生,主要研究方向:机械故障诊断。
基金资助:
国家质检总局科技计划项目(2013QK104);云南省质量技术监督局科技计划项目(2013ynzjkj02)。

Bearing fault diagnosis method based on conditional local mean decomposition and variable predictive model

XU Youcai, WAN Zhou

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650500, China

Received:2015-04-03 Revised:2015-06-04 Online:2015-09-10 Published:2015-09-17

摘要/Abstract

摘要： 针对局部均值分解(LMD)方法在分解非线性、非平稳振动信号过程中存在的模态混淆现象,从而影响故障识别准确性的问题,提出了基于条件局部均值分解方法(CLMD)与模式识别变量预测模型(VPMCD)的故障诊断方法。该方法将数字图像处理的频率分辨率方法与LMD相结合,首先确定振动信号中所有局部极值点的频率分辨率,将振动信号分为低频率分辨率区域和高频率分辨率区域;然后对高频率分辨率区域进行LMD分解,可得若干乘积函数(PF)分量;最后用折线将所有PF分量连接起来,经滑动平均处理可得PF分量,提取PF分量的偏度系数和能量系数构成故障特征向量,用于VPMCD故障识别。将该方法应用于轴承故障诊断,实验结果表明,与LMD方法相比,识别效率提高了8.33%,表明了该方法的有效性和可行性。

关键词: 条件局部均值分解, 局部均值分解, 模态混淆现象, 变量预测模型模式识别, 故障诊断

Abstract: Aiming at the problem that the modal aliasing phenomenon of Local Mean Decomposition (LMD) method in the decomposition process of nonlinear and non-stationary vibration signals, affects the accuracy of identification, a fault diagnosis method based on Conditional Local Mean Decomposition (CLMD) method and Variable Predictive Model Class Discriminate (VPMCD) was proposed. The method combined the frequency resolution method of digital image processing with LMD. Firstly, the frequency resolutions of all local extreme points were calculated, and according to the frequency resolutions of local extreme points, the vibration signals could be divided into the low frequency resolution area and the high frequency resolution area. Secondly, LMD method was used to decompose the high frequency resolution area to get several components of Product Function (PF). Finally, after these PF components were connected by broken line, PF could be got through moving average processing. The skewness coefficient and the energy coefficient of PF could form fault feature vector. VPMCD could use fault feature vector to identify the fault types. This method was applied into bearing fault diagnosis. The experimental results show that the recognition efficiency of the proposed method increases by 8.33%, compared with LMD. As a result, the method is feasible and valid.

Key words: Conditional Local Mean Decomposition (CLMD), Local Mean Decomposition (LMD), modal aliasing phenomenon, Variable Predictive Model Class Discriminate (VPMCD), fault diagnosis

中图分类号:

TP277

许有才, 万舟. 基于条件局部均值分解与变量预测模型的轴承故障诊断方法[J]. 计算机应用, 2015, 35(9): 2606-2610.

XU Youcai, WAN Zhou. Bearing fault diagnosis method based on conditional local mean decomposition and variable predictive model[J]. Journal of Computer Applications, 2015, 35(9): 2606-2610.

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基于条件局部均值分解与变量预测模型的轴承故障诊断方法

Bearing fault diagnosis method based on conditional local mean decomposition and variable predictive model

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