A Novel Fault Diagnosis Method Based on NEEEMD-RUSLP Feature Selection and BTLSTSVM
Author:
Affiliation:
1. School of Information Science and Technology, Yunnan Normal University, Kunming, China
2. Faculty of Information Engineering, Quzhou College of Technology, Quzhou, China
3. School of Big Data, Baoshan University, Baoshan, Yunnan, China
Funder
National Natural Science Foundation of China
Ph.D. Research Startup Foundation of Yunnan Normal University
Fundamental Research Program of Yunnan Province
Project of Educational Commission of Yunnan Province of China
Project of Quzhou Science and Technology Plan
Yunnan Fundamental Research Projects
Baoshan Xingbao Young Talent Training Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10283816.pdf?arnumber=10283816
Reference53 articles.
1. Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning
2. Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved LightGBM
3. Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing
4. Remaining useful life prediction using an integrated Laplacian-LSTM network on machinery components;saufi;Appl Soft Comput,2021
5. Bearing vibration data collected under time-varying rotational speed conditions
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