Data privacy protection: A novel federated transfer learning scheme for bearing fault diagnosis-Reference-Cited by-同舟云学术

Data privacy protection: A novel federated transfer learning scheme for bearing fault diagnosis

Published:2024-05 Issue: Volume:291 Page:111587
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Liu Lilan^ORCID,Yan Zhenhao^ORCID,Zhang Tingting,Gao Zenggui^ORCID,Cai Hongxia,Wang Jinrui

Funder

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Reference39 articles.

1. A novel method based on deep transfer unsupervised learning network for bearing fault diagnosis under variable working condition of unequal quantity;Hao;Knowl. Based. Syst.,2022

2. Class-imbalance privacy-preserving federated learning for decentralized fault diagnosis with biometric authentication;Lu;IEEE Trans. Industr. Inform.,2022

3. Cross-domain privacy- preserving broad network for fault diagnosis of rotating machinery;Shi;Adv. Eng. Inf.,2023

4. Application of weighted contribution rate of nonlinear output frequency response functions to rotor rub-impact;Liu;Mech. Syst. Signal. Process.,2020

5. Application of Convolutional neural network and data preprocessing by mutual dimensionless and similar gram matrix in fault diagnosis;Xiong;IEEE Trans. Industr. Inform.,2022

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1. Enhancing protection in high-dimensional data: Distributed differential privacy with feature selection;Information Processing & Management;2024-11

2. Federated transfer learning-based distributed fault diagnosis method for rolling bearings;Measurement Science and Technology;2024-09-13

3. Enhancing Privacy in Real-Time Stream Processing: Federated Transfer Learning Approaches;2024 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv);2024-06-12