Rolling bearing fault diagnosis using optimal ensemble deep transfer network

Rolling bearing fault diagnosis with unlabeled data is a meaningful yet challenging task. Recently, deep transfer learning methods with maximum mean discrepancy (MMD) have achieved great attention. To further enhance the performance of individual models, this paper proposes an optimal ensemble deep transfer network (OEDTN). The proposed method takes advantage of parameter transfer learning, domain adaptation and ensemble learning. Firstly, different kernel MMDs are used to construct multiple diverse deep transfer networks (DTNs) for feature adaptation. Secondly, parameter transfer learning is applied to initialize these DTNs with a good start point. Finally, ensemble learning is used to combine these DTNs to acquire the final results. Considering no labeled information available for ensemble, a novel comprehensive metric is designed to guide the particle swarm optimization to assign suitable voting weights for each DTN. By this way, the ensemble strategy of OEDTN can be adaptively constructed. Experiments on three bearing test rigs are carried out, and the results show that the proposed method is more effective than the existing methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper proposes an optimal ensemble deep transfer network (OEDTN) for rolling bearing fault diagnosis, which combines parameter transfer learning, domain adaptation, and ensemble learning to achieve better performance. Experimental results show that OEDTN is more effective than existing methods in diagnosing bearing faults.