TAMTL: A Novel Meta-Transfer Learning Approach for Fault Diagnosis of Rotating Machinery

This paper proposes a novel fault diagnosis scheme for rotating machinery based on meta-transfer learning and test augmentation. The model-agnostic meta-learning (MAML) framework is applied to the fault diagnosis problem by dividing the training and testing tasks according to different operating conditions, which allows the user to arbitrarily select the appropriate basic model according to the task requirements. Then, an additional pre-training phase based on meta-transfer learning is designed to improve the comprehensive performance, and a testing stage is introduced to evaluate the generalization performance of the hyperparameters of fine-tuned model. Experimental results on the CWRU dataset demonstrate that the proposed scheme can achieve high accuracy, stability, and efficiency in fault recognition under cross-condition scenarios.