Bearing fault diagnosis based on improved federated learning algorithm
- 20 October 2021
- journal article
- research article
- Published by Springer Nature in Computing
- Vol. 104 (1) , 1-19
Abstract
No abstract availableKeywords
Funding Information
- the national key Research and Development Program of China (2017YFE0123000)
- the Project of Technological Innovation and Application Development of Chongqing Science and Technology Commission of China (cstc2018jszx-cyzd0078)
This publication has 23 references indexed in Scilit:
- Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trainsISA Transactions, 2020
- Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial IntelligenceIEEE Transactions on Industrial Informatics, 2019
- Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and ComparisonEngineering, 2019
- DÏoT: A Federated Self-learning Anomaly Detection System for IoT2019
- A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life PredictionIEEE Access, 2019
- Federated learning-based computation offloading optimization in edge computing-supported internet of things2019
- A Descriptor System Approach for Estimation of Incipient Faults With Application to High-Speed Railway Traction DevicesIEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017
- Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral ImageryRemote Sensing, 2017
- An Adaptive Approach Based on KPCA and SVM for Real-Time Fault Diagnosis of HVCBsIEEE Transactions on Power Delivery, 2011
- A roller bearing fault diagnosis method based on EMD energy entropy and ANNJournal of Sound and Vibration, 2006