A universal transfer network for machinery fault diagnosis
Authors:
- Xiaolei YuXYXiaolei Yu
- Zhibin ZhaoZZZhibin Zhao
- Xingwu ZhangXZXingwu Zhang
- Shaohua TianSTShaohua Tian
- Chee-Keong KwohCKChee-Keong Kwoh
- Xiaoli LiXLXiaoli Li
- Xuefeng ChenXCXuefeng Chen
- 20 June 2023
- journal article
- research article
- Published by Elsevier in Computers in Industry
- Vol. 151
Abstract
No abstract availableKeywords
This publication has 17 references indexed in Scilit:
- Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transformsComputers in Industry, 2021
- Multiscale dynamic fusion prototypical cluster network for fault diagnosis of planetary gearbox under few labeled samplesComputers in Industry, 2020
- Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark studyISA Transactions, 2020
- Learning to Detect Open Classes for Universal Domain Adaptation2020
- Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networksNeural Networks, 2020
- Applications of machine learning to machine fault diagnosis: A review and roadmapMechanical Systems and Signal Processing, 2020
- DOC: Deep Open Classification of Text Documents2017
- Deep CORAL: Correlation Alignment for Deep Domain Adaptation2016
- Adapting Visual Category Models to New Domains2010
- Integrating structured biological data by Kernel Maximum Mean DiscrepancyBioinformatics, 2006