A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion-Reference-Cited by-同舟云学术

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A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Published:2020 Issue: Volume:8 Page:20523-20536
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Tang Xianghong^ORCID,Gu Xin^ORCID,Wang Jiachen^ORCID,He Qiang^ORCID,Zhang Fan^ORCID,Lu Jianguang^ORCID

Funder

Science and Technology Major Project of Guizhou Province

Project of Guizhou High-Level Study Abroad Talents Innovation and Entrepreneurship

China Scholarship Council

Open Fund of Guizhou Provincial Public Big Data Key Laboratory

Guizhou University

Science and Technology Platform

Talent Team Plan of Guizhou Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/08964342.pdf?arnumber=8964342

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2. Deep Learning–Based Inspection Data Mining and Derived Information Fusion for Enhanced Bridge Deterioration Assessment;Journal of Bridge Engineering;2023-08

3. Optimized neural network with self-adaptive MHBO algorithm for fault diagnosis;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2023-05-25

4. Research on mechanical fault diagnosis based on MADS evidence fusion theory;Measurement Science and Technology;2023-04-26

5. An unsupervised transfer learning model based on convolutional auto encoder for non-alcoholic steatohepatitis activity scoring and fibrosis staging of liver histopathological images;Neural Computing and Applications;2023-01-27

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