基于自适应Morlet小波变换滚动轴承声学故障诊断的研究

Sound Signal Testing of Rolling Bearing Based on Adaptive Morlet Wavelet

基于声音信号的测试与分析是滚动轴承故障检测与诊断的一种新方法,提出了基于自适应Morlet小波变换诊断轴承声学信号故障的新方法。首先利用最小Shannon熵对Morlet小波的形状参数进行优化,找到与所测声音信号特征成份最匹配的小波,再对小波系数矩阵进行奇异值分解,通过奇异值与变化尺度的关系曲线得到最佳小波变换尺度,最后对滚动轴承故障信号进行Morlet小波变换进行故障特征提取。结果表明:该方法能有效地从强噪声背景下提取出轴承声学信号的故障。

Diagnosis of rolling bearing faults based on sound signal testing and analysis is a new method,a feature extraction method of sound signal of rolling bearing is raise based on adaptive Morlet wavelet.Firstly,minimum Shannon entropy is used to optimize the Morlet wavelet shape factor in order to match with the impact component.Then,an abrupt information detection method based on the transitional stage of singular curve of wavelet coefficient matrix is used to choose the appropriate scale for the wavelet transformation.Finally,the fault feature of the signal can be extracted using this method.The experimental results shows that the method can extract sound signal fault feature more effectively.