Explainable Deep Ensemble Model for Bearing Fault Diagnosis Under Variable Conditions-Reference-Cited by-同舟云学术

Explainable Deep Ensemble Model for Bearing Fault Diagnosis Under Variable Conditions

Published:2023-08-01 Issue:15 Volume:23 Page:17737-17750
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Chen Zhuyun¹^ORCID,Qin Wu²,He Guolin¹^ORCID,Li Jipu¹^ORCID,Huang Ruyi¹^ORCID,Jin Gang¹^ORCID,Li Weihua¹^ORCID

Affiliation:

1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

2. School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Guangzhou Basic and Applied Basic Research Foundation

Opening Project of Beijing Key Laboratory of Measurement Control of Mechanical and Electrical System Technology, Beijing Information Science Technology University

China Postdoctoral Science Foundation

Technology Project of Jiangxi Provincial Department of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10198840/10144572.pdf?arnumber=10144572

Reference48 articles.

1. A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault Diagnosis

2. Vision-Based Fault Diagnostics Using Explainable Deep Learning With Class Activation Maps

3. A Novel Impact Feature Extraction Method Based on EMD and Sparse Decomposition for Gear Local Fault Diagnosis

4. An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery

5. A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery

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