STFT在AE信号特征提取中的应用  

Application of STFT in feature extraction of acoustic emission signal

作　　者：廖传军[1] 李学军[1] 刘德顺[1] 

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

出　　处：《仪器仪表学报》2008年第9期1862-1867,共6页Chinese Journal of Scientific Instrument

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

摘　　要：机械故障或损伤引发的声发射信号南高频突发脉冲信号和长周期准平稳噪声信号组成,适宜用短时傅里叶变换(STFT)描述其时频特征。本文通过分析典型AE信号及其特征提取,首次将STFT引入声发射故障诊断领域,并提出了AE信号的STFT分析法。通过理论分析和仿真,确定了AE信号STFT的理想窗函数及其参数选择,有效地克服了STFT只用一个固定窗分析多尺度信号的缺陷。将STFT用于声发射检测的滚动轴承损伤类型及部件的识别,诊断结果十分准确、清晰和直观。仿真分析和实验研究均表明了STFT能有效提取AE信号的特征,为AE信号的波形分析开辟了一条有效的途径。Acoustic emission （AE） signals initiating by mechanical faults or damages compose of two types of signals, high frequency burst impulse signal and long period qusi-stationary noise signal, of which Short Time Fourier Transform （STFT） can describe the time-frequency characteristics well. By analyzing the characteristics and feature extraction of typical AE signals, the paper applies STFT for fault diagnosis based on AE technique for the first time, and puts forwards the STFT analysis method of AE signal. By theory analysis and simulations, the perfect window function used in STFT of AE signals is defined, and the parameter related with the window function is selected also. The flaw of STFT applying for signal analysis is solved effectively. When applying STFT for fault diagnosis of rolling bearings based on AE techniques, the results are quite accurate, clear and audio-visual. Both simulations and experimental research prove that STFT can be used for feature extraction of AE signals well, and provides a very effective path for waveform analysis of AE signals.

关 键 词：AE STFT 特征提取 窗函数 故障诊断 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]