An improved deep convolutional neural network with multiscale convolution kernels for fault diagnosis of rolling bearing

Abstract

With the help of multiple layers of nonlinear mapping capabilities, deep neural network models can adaptively extract fault features and diagnose faults, which improve the efficiency and accuracy of fault diagnosis. Based on Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN), this paper proposes an improved model named Deep Convolutional Neural Networks with Multiscale First-layer Convolution Kernels (MDCNN). The proposed method uses 1D convolution kernels of different sizes to extract multiscale features from original vibration signals. Afterwards, to achieve feature fusion, different feature maps are reduced to the same size through adaptive convolution operations. Finally, through the learning of the multi-layer network, intelligent fault diagnosis from the signal to the health state is realized. A test based on CWRU dataset is performed to verify the accuracy of the proposed method for rolling bearing fault diagnosis. Results indicate that MDCNN shows higher performance than WDCNN.