Multi-sensor signals with parallel attention convolutional neural network for bearing fault diagnosis

Abstract

Rolling bearing fault signals are non-smooth, non-linear, and susceptible to background noise interference. A feature layer fusion model combining multi-sensor signals and parallel attention convolutional neural networks is proposed and applied to the fault diagnosis work of rolling bearings. First, a multi-channel parallel convolutional neural network model is constructed according to the number of sensors, and the multi-sensor signals are fed to each parallel channel separately. Second, due to the different strengths of shock features within each channel and signal, the attention mechanism is introduced into each parallel channel, the fault features with strong shock characteristics are extracted, and the feature extraction capability for different sensor signals is improved. Finally, the extracted feature information is fused in the concatenate layer, the fused features are input to the fully connected layer, and the diagnosis results are output through the Softmax layer. The experimental results show that the model can effectively fuse multi-sensor signal features, and its recognition accuracy is greatly improved over that of the single sensor, providing a feasible method for bearing fault diagnosis.