Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer
出版年份 2023 全文链接
标题
Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer
作者
关键词
-
出版物
NEUROCOMPUTING
Volume 548, Issue -, Pages 126391
出版商
Elsevier BV
发表日期
2023-06-03
DOI
10.1016/j.neucom.2023.126391
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- A double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosis
- (2021) Yafei Deng et al. COMPUTERS IN INDUSTRY
- Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning
- (2021) Wei Zhang et al. IEEE Transactions on Industrial Informatics
- Joint distribution adaptation with diverse feature aggregation: A new transfer learning framework for bearing diagnosis across different machines
- (2021) Shiyao Jia et al. MEASUREMENT
- A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
- (2021) Weihua Li et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis with Unlabeled or Insufficient Labeled Data
- (2020) Cheng Cheng et al. NEUROCOMPUTING
- A Deep Adversarial Transfer Learning Network for Machinery Emerging Fault Detection
- (2020) Jipu Li et al. IEEE SENSORS JOURNAL
- A systematic review of deep transfer learning for machinery fault diagnosis
- (2020) Chuan Li et al. NEUROCOMPUTING
- An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
- (2019) Bin Yang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fault diagnostics between different type of components: A transfer learning approach
- (2019) Xudong Li et al. APPLIED SOFT COMPUTING
- Retraining Strategy-Based Domain Adaption Network for Intelligent Fault Diagnosis
- (2019) Yan Song et al. IEEE Transactions on Industrial Informatics
- Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks
- (2018) Xiang Li et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning
- (2018) Siyu Shao et al. IEEE Transactions on Industrial Informatics
- A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
- (2017) Long Wen et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions
- (2017) Ran Zhang et al. IEEE Access
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started