声信号分析方法在轴承故障诊断中的应用

Machine Sound Using Wavelet and Application in Rolling Bearing Fault Diagnosis

摘要: 由于轴承故障声信号的混响及临近的机械设备的噪声，造成声信号的频域分析很困难．通过小波变换原理，对滚动轴承故障声信号进行时频分析．通过对声信号的多尺度分解，分离出由故障造成的声信号突变．实验结果表明，较之以往的时域、频域信号处理技术，该方法对声音信号分解更趋合理，是一种可靠和有效的滚动轴承故障诊断新方法．

Abstract: Machine sound always carries information about the working of the machine. But in many cases, the sound has a very low SNR, so it is very difficult to make time-frequency analyse of sound signal. A denoising method based on wavelet technology is given. Based on wavelet decomposition, sound signal caused by mechanical diagnosis can be separated. Experimentation tests that this is an effective method to diagnose fault of rolling bearing comparing with other fault diagnosis methods.