Machine Sound Using Wavelet and Application in Rolling Bearing Fault Diagnosis
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摘要: 由于軸承故障聲信號的混響及臨近的機械設備的噪聲，造成聲信號的頻域分析很困難．通過小波變換原理，對滾動軸承故障聲信號進行時頻分析．通過對聲信號的多尺度分解，分離出由故障造成的聲信號突變．實驗結果表明，較之以往的時域、頻域信號處理技術，該方法對聲音信號分解更趨合理，是一種可靠和有效的滾動軸承故障診斷新方法．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.
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Key words:
- sound signal /
- fault diagnosis /
- wavelet /
- rolling bearing
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