A Bearing Fault Diagnosis Method Based on Improved Mutual Dimensionless and Deep Learning
Author:
Affiliation:
1. School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China
2. School of Computer Science, Guangdong University of Technology, Guangzhou, China
3. Foreign Language Department, Jieyang Polytechnic, Jieyang, China
Funder
Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province of China
Special Projects in Key Fields of Normal University of Guangdong Province
Key (Natural) Project of Guangdong Provincial
Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10217055/10109681.pdf?arnumber=10109681
Reference40 articles.
1. A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests
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3. Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis
4. A novel mathematical morphology spectrum entropy based on scale-adaptive techniques
5. Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows
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