A Novel Local Binary Temporal Convolutional Neural Network for Bearing Fault Diagnosis

In bearing fault diagnosis, the faulty data are generally limited due to the high cost of fault signal collection. Considering the excessive parameters in the traditional convolutional neural network (CNN), such a limited data issue can cause overfitting problem during the model training, eventually resulting in poor fault diagnosis performance. To resolve the overfitting issue and elevate the diagnostic accuracy of the conventional methods, a novel fault diagnosis method based on local binary temporal CNN (LBTCNN) is proposed in this article. In the proposed LBTCNN, a novel temporal module with dilated causal convolution for deep feature extraction is proposed to increase model depth under limited model parameters, and a local binary convolution (LBC) layer is adopted to reduce the computational parameters. To evaluate the effectiveness of the proposed method, several experiments under different scenarios such as limited samples and different noise levels are conducted on two datasets, including the rolling bearing accelerated life test dataset of Xi'an Jiaotong University and Changxing Sumyoung Technology (XJTU-SY), and the motor bearing dataset of Case Western Reserve University (CWRU). The comparison results demonstrate that the LBTCNN method is superior over six other prominent fault diagnosis approaches under different bearing operation stages, different training samples, and different signal-to-noise ratios (SNRs).