Intelligent Fault Diagnosis of Rolling Bearings Using Efficient and Lightweight ResNet Networks Based on an Attention Mechanism (September 2022)

Abstract

Focusing on the problems of complex structure and low feature extraction efficiency that exist in some traditional neural network algorithms, an improved convolutional neural network (CNN), combined with a convolutional attention mechanism, is proposed to improve the network feature extraction efficiency and reduce the network complexity. First, the convolutional block attention module (CBAM) was introduced, which can strengthen the extraction of useful features from the data, thereby improving the network feature extraction efficiency. Second, the residual structure was optimized, and the original multilayer convolution was simplified to a single-layer convolution, which reduces the complexity of the model and improves the diagnostic efficiency. Then, the rectified linear unit (ReLU) activation function in the model was replaced with the better-performing exponential linear unit (ELU) activation function. Finally, the overall structure of the residual network (ResNet) model was optimized. The CBAM-ResNet network model was based on the traditional ResNet18 model and consists of a CBAM module and the optimized ResNet18 model. Finally, experiments were using sensor data collected from rolling bearing test benches. The experimental results show that the feature extraction capability and efficiency of the new model are better than those of several other classic deep network models and the original network model, and this reduces the time complexity and model size while also maintaining a high accuracy rate.