Intelligent fault diagnosis and visual interpretability of rotating machinery based on residual neural network. (15th June 2022)

• Record Type: Journal Article
• Authors: Yu, Shihang; Wang, Min; Pang, Shanchen; Song, Limei; Qiao, Sibo
• Is Part Of: Measurement. Volume 196(2022)
• Publication Date: 2022-06-15
• Subjects: Intelligent fault diagnosis -- Interpretability -- Residual neural network; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111228
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Accuracy of machinery fault diagnosis and interpretability of diagnosis methods are fundamental to safe operation of machinery and help to improve the universality of the model. Mechanical vibration signals can reflect the operating state of the machine. Therefore, to improve the accuracy of fault diagnosis, this paper constructs a 6-layer residual neural network (ResNet06), which embeds two residual blocks to fully extract features of the mechanical vibration signals. Then, we use the gradient-based class activation map (Grad-CAM) and eigenvector-based class activation map (Eigen-CAM) to interpret the ResNet06 visually and to verify the ResNet06 correctness. Experimental results indicate that the fault diagnosis accuracy of our proposed model can reach almost 100%, and it can be seen that the model can accurately capture the fault points by the visualization of the model. Highlights: Based on features of vibration signals, we construct a 6-layer residual neural model. Use the Grad-CAM and Eigen-CAM to interpret the ResNet06. Visualize advanced features using t-SNE. Analyze the clustering of advanced features.