小波阈值分析与EMD结合的机械设备故障诊断方法  

Fault Diagnosis Method of Wavelet Threshold Analysis Combined with EMD for Mechanical Equipment

作　　者：王立东[1] 张凯[1] 

机构地区：[1]辽宁科技大学电子与信息工程学院,辽宁鞍山114051

出　　处：《机械传动》2015年第10期104-107,145,共5页Journal of Mechanical Transmission

基　　金：鞍山市科技计划项目资助(20131203)

摘　　要：针对采集的机械设备故障信号中夹杂着噪声干扰的问题,提出一种基于贝叶斯估计的小波收缩新阈值和EMD结合的机械设备故障诊断方法。新阈值的选取考虑了故障信号经小波变换后在不同尺度上的去噪特性,更符合噪声在各层中的分布情况;改进阈值函数对故障信号进行降噪处理,然后以互相关系数和峭度准则提取经EMD分解降噪信号的分量,突出高频共振部分,避免了IMF分量选择的盲目性。通过对仿真信号分析和实例分析,结果能够准确地检测出设备故障。According to the problem of noise interference in the mechanical equipment fault signal acquisition,a novel mechanical equipment fault diagnosis method of wavelet shrinkage threshold based on Bayesian estimation combined with EMD is proposed. The fault signal denoising characteristics of different scales are considered in proposed method. The new threshold is suitable for the situation of noise distribution. The noise reduction can be gotten by improving the threshold function. The signal components decomposed and denoised by EMD is extracted with cross- correlation and kurtosis criterion to highlight the high- frequency resonance components,the blindness of IMF component selection is avoided. The results of analysis applied to simulated signal and the measured signal show that the equipment fault can be detected accurately.

关 键 词：阈值 故障 经验模态分解(EMD) 机械 

分 类 号：TH165.3[机械工程—机械制造及自动化]