A fault diagnosis for rolling bearing based on multilevel denoising method and improved deep residual network

Aiming at the problem that weak faults in rolling bearings make effective fault diagnosis difficult under strong noise, this paper proposes a multilevel denoising technology based on improved singular value decomposition (ISVD) and intrinsic timescale decomposition (ITD), combined with an improved deep residual network (ResNet), for fault diagnosis in rolling bearings. Firstly, the difference ratio (DR) index is introduced to optimize singular value decomposition, combined with ITD for multilevel denoising of strong noise signals. Effective fault information in bearing vibration signals is extracted and converted into grayscale images. Secondly, the multi-scale feature extraction module (MFE-Module) is introduced to enhance the feature extraction capability of ResNet, and the support vector machine (SVM) is used instead of the Softmax function to identify and classify the fault features. The experimental results indicate that, compared with other methods, the proposed method can more accurately realize the fault diagnosis of rolling bearings in strong noise environments.