Global-Local Continual Transfer Network for Intelligent Fault Diagnosis of Rotating Machinery

Existing fault diagnosis methods face three fundamental challenges when deployed in the dynamic environment, including insufficient continuous diagnostic capability, poor model generalization performance, and inadequate data privacy protection. To address these problems, we develop a novel continual fault diagnosis framework named Global-Local Continual Transfer Network (GLCTN) for fault classification of unlabeled target samples under different working conditions without any source samples. Specifically, a consistency loss and a mutual information loss are introduced in the proposed GLCTN to transfer the learned diagnostic knowledge. Moreover, a dual-speed optimization strategy is utilized to preserve the acquired diagnostic knowledge and to endow the model with the ability to acquire new knowledge. Validation experiments conducted on an automobile transmission dataset demonstrate that the proposed GLCTN achieves satisfactory diagnostic performance on multiple continuous transfer diagnostic tasks.