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Cross-Task Fault Diagnosis Based on Deep Domain Adaptation With Local Feature Learning
- Source :
- IEEE Access, Vol 8, Pp 127546-127559 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Data based intelligent fault diagnosis is a critical tool for the healthy development of industry process. In actual industrial production, there are often a few or even no labeled samples for target monitoring problem, while large amounts of training data come from different but related diagnosis task under variable working conditions. To utilize the labeled data on related issue for better monitoring performance, the cross-task fault diagnosis based on deep domain adaptation with local feature leaning is proposed. In our strategy, the two-stream stacked autoencoders based deep architecture is used to extract transferable features of collected data across the target diagnosis task domain and the related data-rich monitoring task domain. Then, the maximum mean discrepancy is introduced to establish a deep transfer diagnosis model. Moreover, to further optimize the model, we propose the local feature learning, which can make test data with better intra-class compactness and inter-class separability. Eventually, the proposed method is verified on the Tennessee Eastman process and the rolling bearing data, the results show that our approach achieves positive performance for cross-task fault diagnosis problems.
- Subjects :
- maximum mean discrepancy
General Computer Science
Computer science
02 engineering and technology
Machine learning
computer.software_genre
Fault (power engineering)
industrial process
Domain (software engineering)
Task (project management)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
deep domain adaptation
Training set
business.industry
020208 electrical & electronic engineering
General Engineering
Process (computing)
stacked autoencoders
fault diagnosis
Variable (computer science)
Cross-task
Feature (computer vision)
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
Feature learning
computer
lcsh:TK1-9971
Test data
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....8eae2b3c6e6b8ab6e70da415a78cb6d5