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Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review
- Source :
- IEEE Access, Vol 7, Pp 129260-129290 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis.
- Subjects :
- General Computer Science
Computer science
domain adaptation
review
02 engineering and technology
Cross-domain
transfer learning
Machine learning
computer.software_genre
Fault (power engineering)
Domain (software engineering)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Training set
business.industry
020208 electrical & electronic engineering
General Engineering
fault diagnosis
Dilemma
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
Transfer of learning
business
computer
Knowledge transfer
lcsh:TK1-9971
Test data
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....4f372f811c7e6973dbff3b2522fa2ba3