基于解析小波变换的奇异性检测和特征提取

为提高信号奇异性检测的精度和故障特征提取的有效性，利用信号和噪声的小波变换模极大值沿尺度方向的不同传播特性，提出了一种通过解析小波极大模重构进行信号奇异性检测和滤噪的方法，并将解析小波分析引入机械故障诊断中.分别采用实小波极大模和解析小波极大模分析汽车主减速器性能试验机上采集的几种故障振动信号，并进行主减速器故障诊断.试验结果表明，解析小波极大模相比实小波极大模具有更好的奇异性检测效果，能够突出故障特征，从而有效提高故障诊断的准确性.

Singularity detection and feature extraction based on analytic wavelet transform

A singularity detection and denoising method based on analytic wavelet transform(AWT) and signal reconstruction was proposed to improve the accuracy of signal singularity detection and the efficiency of fault diagnosis.According to the difference of propagation characteristics of wavelet transform modulus maximum(WTMM) of signal and noise along the scale direction,signal denoising and fault feature extraction were realized.Singularity detection and denoising based on AWT was applied to the vibration signals of running machines.Signals sampled under several conditions in a main reducer performance test bed were analyzed,and the fault diagnosis of the main reducer was conducted by analytic WTMM and real WTMM respectively.Experimental results show that the singularity detection using the modulus maximum of an analytic wavelet is better than that of a real wavelet,and that the fault feature can be distinguished more obviously and accurately.