WavCapsNet: An Interpretable Intelligent Compound Fault Diagnosis Method by Backward Tracking
Author:
Affiliation:
1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
2. School of Engineering, The University of British Columbia at Okanagan, Kelowna, Canada
3. Pazhou Laboratory, Guangzhou, China
Funder
National Natural Science Foundation of China
Natural Science Foundations of Guangdong Province-China
China Postdoctoral Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10143698.pdf?arnumber=10143698
Reference44 articles.
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4. Learning with capsules: A survey;de sousa ribeiro;arXiv 2206 02664,2022
5. Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
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