Work

Federated Learning for Rolling Bearing Fault Diagnosis: A RepViT-Enabled Approach

Year: 2025

Type: conference-paper

Abstract: Federated learning (FL) has emerged as a promising approach for fault diagnosis in rotating machinery, addressing data privacy and availability challenges. This paper presents a federated learning-based fault diagnosis framework, leveraging lightweight Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to diagnose faults under varying operating conditions accurately. The proposed methodology utilizes the RepViT model, a hybrid architecture combining MobileNetV3_L and lightweight ViTs, to achieve efficient fault diagnosis on mobile devices. Experiments conducted on the HUST-bearing dataset demonstrate the effectiveness of the proposed method, achieving high diagnostic accuracy even with limited data. The study further explores the impact of varying the number of participating clients and the robustness of the model under different client availability scenarios. Results show that the federated approach maintains high diagnostic performance while preserving data privacy, making it a viable solution for industrial applications.

DOI: 10.58286/31020

Authors: Cheng Cheng, Yiwei Cheng, Wenwei Liu, Yuanhang Wang

Institutions: China University of Geosciences, China Electronic Product Reliability and Environmental Test Institute, Shenzhen Technology University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Funders: National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province

Awards: G1184675653, G7023856084