Feature Extraction for Low-Speed Bearing Fault Diagnosis Based on Spectral Amplitude Modulation and Wavelet Threshold Denoising

To address the issue of difficult extraction of bearing fault features caused by weak fault features and strong environmental noise in low-speed, a low-speed bearing fault diagnosis method based on wavelet threshold denoising and spectral amplitude modulation is proposed. The proposed method effectively overcomes the limitation that the traditional spectral amplitude modulation is greatly affected by noise in low-speed. Firstly, the raw signal is subjected to wavelet threshold denoising to reduce the interference of strong background noise, thereby obtaining the denoised signal. Secondly, the denoised signal is subjected to spectral amplitude modulation to enhance the bearing fault impulses. Finally, the envelope spectrum is normalized to facilitate the visual display of fault feature frequencies. The proposed method is analyzed through simulated and experimental signals in low-speed. The experimental results indicate that the proposed method can reduce noise interference and effectively extract fault features in low-speed.