适于声发射信号故障特征提取的小波函数  

Wavelet Function Suitable for Fault Feature Extraction of Acoustic Emission Signal

作　　者：李学军[1] 廖传军[1] 褚福磊[2] 

机构地区：[1]湖南科技大学机械设备健康维护湖南省重点实验室,湘潭411201 [2]清华大学精密仪器与机械学系,北京100084

出　　处：《机械工程学报》2008年第3期177-181,共5页Journal of Mechanical Engineering

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

摘　　要：小波分析具有很强的弱信号检测能力,适于提取声发射信号的故障特征,但是在目前声发射信号的小波分析中,所普遍采用的小波函数诊断效果欠佳,亟需改进和优化。在分析典型机械故障声发射信号特征的基础上,根据损伤型声发射信号故障特征的提取原理,通过连续小波基函数的构造方法,设计了一种适于声发射信号故障特征提取的小波基函数。将该函数与普遍使用的Daubechies小波同时用于声发射检测的滚动轴承损伤类型及部件的识别,结果表明前者的诊断效果更加清晰、准确、可靠。理论分析和试验研究均证明了所构造小波函数的科学性和有效性。It is pointed that wavelet analysis has powerful ability for weak signal detection, which helps it to be used for detection and fault diagnosis of acoustic emission signals well. However, in wavelet analysis of fault acoustic emission（AE） signals nowadays, the diagnosis results using the general wavelet functions are not the best, and some new wavelet functions need to be created at once. By analyzing the characteristics of typical AE signals initiated by mechanical faults or damages, and according to the extracting principle of fault characters of AE signals and the construction method of continuous wavelet function, a wavelet function is designed. When applying the function and Daubechies wavelet for fault diagnosis of rolling bearings based on AE technique at the same time, the results using the former are more clear, accurate and reliable. Both theory analysis and experiment research prove that the wavelet function is scientific and effective.

关 键 词：小波函数 小波变换 声发射 故障诊断 特征提取 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]