Collaborative deep learning framework for fault diagnosis in distributed complex systems

In distributed complex systems, condition monitoring and fault diagnosis have received considerable attention, especially for recent developments of data-driven methods with deep learning structures, which greatly enhance the performance as of superior representation capacity over big data. To apply these methods, massive data needs to be collected from distributed systems, requiring high costs for data transmission and causing more and more concerns on privacy issues. For the naturally distributed data in such scenario, this work presents a novel collaborative deep learning framework with the idea that the features, as representations of data, can be transmitted through latent parameters of deep learning structure while the raw data won?t be shared in the distributed network. Based on the collaborative learning setup, the proposed framework adopts a secure communicating strategy with no need of transmitting raw data, and obtains a consensus for distributed deep learning models that can be geographically located. To validate the proposed scheme, four case studies are carried out and the results show that it is able to improve the diagnosis accuracy compared with local learning models. Also, it is robust and adaptive for diagnosis problems with data that is imbalanced or from different distributions. ? 2021 Elsevier Ltd. All rights reserved.

Automated summary

In distributed complex systems, a novel collaborative deep learning framework is proposed, which aims to improve diagnosis accuracy by transmitting latent parameters instead of sharing raw data, demonstrating robustness and adaptability.