A Novel Convolutional Neural Network for Low-Speed Structural Fault Diagnosis Under Different Operating Condition and Its Understanding via Visualization
Haihong Tang, Zhiqiang Liao, Peng Chen, Dunwen Zuo, Yi Sheng
Abstract
A novel method of intelligent diagnosis is proposed for structural faults in low-speed rotating machinery. It uses a hybrid scheme to automatically identify health states in a complex mechanical system by combining an improved mode decomposition approach, a Gramian angular summation field, and a convolutional neural network. The proposed method is tested with a data set affected by noise to evaluate its performance and generalizability that are both essential in fault diagnosis. Experimental results show that the proposed scheme is superior to traditional machine learning methods. It not only provides a more efficient and widely applicable way to learn intrinsic fault signatures, but it also has enhanced feature learning capability to improve precision and generalizability. Finally, the reasons for the method's high performance are analyzed to determine the best general features for an adaptive classifier that is generalizable to diverse operating conditions.