Research Area:  Machine Learning

Abstract:

Due to the limitations of data quality and quantity of a single industrial user, the development of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the perspectives of both academic research and engineering applications in the recent years. Collaborative fault diagnosis model development has been receiving increasing attention, where the distributed data at different users are explored simultaneously. However, data security and privacy are the major industrial concerns, which have not been well addressed in the literature. In this paper, a blockchain-based decentralized federated transfer learning method is proposed for collaborative machinery fault diagnosis. A tailored committee consensus scheme is designed for optimization of the model aggregation process, and a source data-free transfer learning method is further proposed. After global model initialization, the fault diagnosis model can be built through iterations of committee member selection, performance evaluation, transfer learning, model aggregation and blockchain updates. The experiments on two decentralized fault diagnosis datasets are implemented for validations, and higher than 90% testing accuracies can be generally achieved. The experimental results indicate the proposed method is effective in data privacy-preserving collaborative fault diagnosis of multiple users. It offers a promising tool for applications in the real industrial scenarios.

Keywords:  

Author(s) Name:  Wei Zhang, Ziwei Wang, Xiang Li

Publisher name:  ScienceDirect

DOI:  10.1016/j.ress.2022.108885

Volume Information:  Volume 229, (2023)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0951832022005026