Bearing Faulty Prediction Method Based on Federated Transfer Learning and Knowledge Distillation

Abstract

In this paper, a novel bearing faulty prediction method based on federated transfer learning and knowledge distillation is proposed with three stages: (1) a “signal to image” conversion method based on the continuous wavelet transform is used as the data pre-processing method to satisfy the input characteristic of the proposed faulty prediction model; (2) a novel multi-source based federated transfer learning method is introduced to acquire knowledge from multiple different but related areas, enhancing the generalization ability of the proposed model; and (3) a novel multi-teacher-based knowledge distillation is introduced as the knowledge transference way to transfer multi-source knowledge with dynamic importance weighting, releasing the target data requirement and the target model parameter size, which makes it possible for the edge-computing based deployment. The effectiveness of the proposed bearing faulty prediction approach is evaluated on two case studies of two public datasets offered by the Case Western Reserve University and the Paderborn University, respectively. The evaluation result shows that the proposed approach outperforms other state-of-the-art faulty prediction approaches in terms of higher accuracy and lower parameter size with limited labeled target data.

Keywords

• knowledge distillation
• federated transfer learning
• parameter size
• knowledge transference
• edge-computing deployment