Intelligent Fault Diagnosis for Electric Drive Systems Based on Federated Learning and Edge Computing

DOI

10.2991/978-94-6463-823-3_109How to use a DOI?

Keywords

Electric drive system; Fault diagnosis; Federated Learning; Edge Computing

Abstract

As the core power unit of modern industrial equipment, such as new energy vehicles, the fault diagnosis of the electric drive system is very important to the safety and reliability of the equipment. However, the traditional fault diagnosis methods lack stability, and the centralized model also has many problems. Aiming at the requirements of data privacy protection and real-time diagnosis in distributed industrial scenarios, an intelligent fault diagnosis framework based on the collaboration of federated learning and edge computing was proposed. The framework realizes data privacy protection through distributed training of federated learning, and combines real-time processing of edge computing to optimize delay and energy consumption. A lightweight Transformer model, dynamic federation strategy, and multimodal anti-noise technology are designed to achieve 23ms inference delay on edge devices such as STM32H7. This collaborative architecture significantly improves the diagnostic efficiency and data security by deeply integrating distributed training and edge real-time processing. This research provides an innovative solution for the intelligent and safe diagnosis of electric drive systems and lays a theoretical and technical foundation for fault diagnosis technology in different scenarios.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bingyue Wang
PY  - 2025
DA  - 2025/08/31
TI  - Intelligent Fault Diagnosis for Electric Drive Systems Based on Federated Learning and Edge Computing
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 1140
EP  - 1148
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_109
DO  - 10.2991/978-94-6463-823-3_109
ID  - Wang2025
ER  -