小波尺度谱在AE信号特征提取中的应用  

Application of wavelet scalogram in feature extraction of acoustic emission signal

作　　者：肖思文[1] 廖传军[1] 李学军[1] 

机构地区：[1]湖南科技大学湖南省机械设备健康维护重点实验室,湖南湘潭411201

出　　处：《中国工程科学》2008年第11期69-75,共7页Strategic Study of CAE

基　　金：国家自然科学基金资助项目(50675066);湖南省科技计划资助项目(2007FJ3025)

摘　　要：通过分析典型声发射信号及其特征提取,将小波尺度谱引入到声发射故障诊断领域,首次提出了声发射信号的小波尺度谱分析法。给出了小波基函数及其参数的选取,克服了声发射信号小波尺度谱的时、频分辨率不能同时达到最好的缺陷。将小波尺度谱用于声发射检测的滚动轴承损伤类型及部件的识别,诊断结果十分直观、清晰、准确。仿真分析和实验研究均表明小波尺度谱能有效应用于基于声发射技术的状态监测与故障诊断。Acoustic emission （AE） signals initiated by mechanical faults or damages is composed of two types of signals： high frequency burst impulse signal and long period quasi-stationary noise signal. Wavelet scalogram has a particular time-frequency localization, which helps it to be well used for describing the time-frequency characteristics of AE signals. By analyzing the characteristics and feature extraction of typical AE signals, the paper applies wavelet sealogram for fault diagnosis based on AE technique, and presents the wavelet scalogram analysis method of AE signal for the first time. By theoretical analysis and simulation, the wavelet basis function and parameter related to the function are defined. So the limitation that best time resolution and frequency resolution of wavelet scalogram cannot get at the same time is overcome effectively. When applying wavelet scalogram for fault diagnosis of rolling bearings based on AE techniques, the results are quite visualized, clear and accurate. Both simulations and experimental research prove that wavelet scalogram can be used for condition monitoring and fault diagnosis based on AE detection well.

关 键 词：小波尺度谱 声发射 特征提取 故障诊断 滚动轴承 

分 类 号：TG115.28[金属学及工艺—物理冶金] TH113[金属学及工艺—金属学]