Early Fault Diagnosis of Rolling Bearing Based on Threshold Acquisition U-Net

Abstract

Considering the problem that the early fault signal of rolling bearing is easily interfered with by background information, such as noise, and it is difficult to extract fault features, a method of rolling bearing early fault diagnosis based on the threshold acquisition U-Net (TA-UNet) is proposed. First, to improve the feature extraction ability of U-Net, the channel spatial threshold acquisition network (CS-TAN) and the dilated convolution module (DCM) based on different dilated rate combinations are introduced into the U-Net to construct the TA-UNet. Among them, the CS-TAN can adaptively learn the threshold, reduce the interference of noise in the signal, and the DCM can improve the multi-scale feature extraction ability of the network. Then, the TA-UNet is used for early fault diagnosis, and the method is divided into two steps: The model training phase and the vibration signal fault feature extraction phase. In the first step, additive gaussian white noise is added to the vibration signal to obtain the noise-added vibration signal, and the TA-UNet is trained to learn how to denoise the noise-added vibration signal. In the second step, the trained TA-UNet is used to extract the fault features of vibration signals and diagnose the early fault types of rolling bearing. The two-step method solves the problem that U-Net, as a supervised neural network, needs corresponding labeled data to be trained, as it realizes the fault diagnosis of unlabeled data. The feature extraction capability of the TA-UNet is evaluated by denoising the simulated signal of rolling bearing. The effectiveness of the proposed diagnostic method is demonstrated by the early fault diagnosis of open-source datasets.