Multisource Domain Feature Adaptation Network for Bearing Fault Diagnosis Under Time-Varying Working Conditions
Author:
Affiliation:
1. School of Rail Transportation, Soochow University, Suzhou, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09761066.pdf?arnumber=9761066
Reference42 articles.
1. Deep domain confusion: Maximizing for domain invariance;tzeng;arXiv 1412 3474,2014
2. Globally Localized Multisource Domain Adaptation for Cross-Domain Fault Diagnosis With Category Shift
3. Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults
4. A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines
5. Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources
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