Application of wavelet transform modulus maximum detection in early fault diagnosis of rolling bearings

Abstract Rolling bearings are the key parts of rotating machinery, and they are also easy to be damaged. In this paper, high-precision sensors are used to collect the vibration signals of rolling bearings, and the wavelet soft threshold denoising method is adopted to eliminate the noise in the signals and highlight useful signals of the obtained signals. Based on the detection features of wavelet transform modulus maximum, the method for extracting fault features submerged in a noisy background is discussed, and the shortcomings of the traditional Fourier transform are also pointed out. The research shows that the wavelet modulus maximum detection technology can highlight the early bearing fault features, and there is a mapping relationship between the extracted fault features and common bearing fault types. The results of this study can be used as a reference for researchers engaged in bearing fault detection and diagnosis.