Dynamic domain adaptive ensemble for intelligent fault diagnosis of machinery

Abstract

The cross-domain intelligent fault diagnosis (IFD) using unlabeled data has attracted more and more attention. However, most researchers focus on the improvement of single domain adaptive method (DAM). How to make full use of existing DAMs to improve the accuracy and generalization of the IFD model is a challenging problem. As a potential solution, a general dynamic domain adaptive ensemble (DDAE) framework is proposed. By introducing the optimal adaptation factor and combining with the proposed dynamic adaptive evaluating strategy, the DDAE can quantitatively evaluate the importance of different DAMs, and dynamically adjust the weight of DAMs during the training process. By this way, the ensemble strategy can be constructed adaptively within the model. We also design a feasible DDAE-based neural network model by integrating three different DAMs. Extensive experimental analysis indicates that the diagnostic performance of the model is superior to existing deep learning and transfer learning methods.