Knowledge and Data Dual-Driven Fault Diagnosis in Industrial Scenarios: A Survey-Reference-Cited by-同舟云学术

Knowledge and Data Dual-Driven Fault Diagnosis in Industrial Scenarios: A Survey

Published:2024-06-01 Issue:11 Volume:11 Page:19256-19277
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wang Yimeng¹^ORCID,Shen Jie²^ORCID,Yang Shusen³^ORCID,Han Qing²,Zhao Cong²^ORCID,Zhao Peng²^ORCID,Ren Xuebin²^ORCID

Affiliation:

1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China

2. National Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong University, Xi’an, Shaanxi, China

3. National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi under Program

Science and Technology Plan Project of Henan province under Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10536937/10504763.pdf?arnumber=10504763

Reference115 articles.

1. Industry 4.0 and Industry 5.0—Inception, conception and perception

2. Industry 4.0: a tertiary literature review

3. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

4. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery

5. Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review

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