Application of Spectral Amplitude Modulation in Low-Speed Bearing Fault Diagnosis

A rolling bearing serves as a crucial component in rotating machines, and its malfunction can cause equipment failure. Therefore, extract fault characteristics of rolling bearings is very important. When there are complex interference frequencies in the rolling bearing signals, the fault characteristic signals will be difficult to identify. For the above issues, this article combines the wavelet threshold denoising and the spectral amplitude modulation (SAM) algorithm to apply to the low-speed bearing fault diagnosis. Firstly, perform wavelet thresholding denoising on the raw signals to obtain denoising signals. Secondly, the modified signals are obtained by SAM of the denoising signals. Ultimately, the extraction of fault features is accomplished through the application of envelope analysis to the modified signals. The proposed method is tested using experimental signals. The outcomes of the experiments validate the effectiveness of the proposed methodology in diagnosing bearing faults under low-speed operating conditions.