Dynamic Focusing Network for Semisupervised Mechanical Fault Diagnosis of Rotating Machinery

The key components of the rotating machinery, such as gears and bearings, are prone to damage owing to long-term complex and harsh working situations. This study investigates the weight distribution of neural networks, and finds that the response of the network to the input is uneven, indicating that data-driven models tend to learn more information from certain local parts of the input. Based on this discovery, a novel attention mechanism, namely dynamic focusing, is proposed. The dynamic focusing mechanism highlights local information with important features to extract the discriminative features of key frequency bands. In addition, insufficient labeled data presents challenges for fault diagnosis in the practical. A semisupervised learning method based on mutual information is proposed to solve this problem. The effectiveness of the proposed method is verified by the Case Western Reserve University public dataset as well as the Gearbox Dynamic Simulator dataset obtained in our laboratory. The experimental results show that the proposed method has considerable advantages compared to existing deep learning methods, with test accuracy ranging from 95.31% to 100%.