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A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

作者:Yan, Wenhao[1];Wang, Jing[1];Lu, Shan[2];Zhou, Meng[1];Peng, Xin[3]

机构:[1]North China Univ Technol, Sch Elect & Control Engn, Beijing 100043, Peoples R China;[2]Shenzhen Polytech, Inst Intelligence Sci & Engn, Shenzhen 518055, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份:2023

卷号:11

期号:2

外文期刊名:PROCESSES

收录:;EI(收录号:20241215778077);WOS:【SCI-EXPANDED(收录号:WOS:000942251900001)】;

基金:This research is funded by the National Natural Science Foundation of China (61973023, 62273007, 62003220), Innovation Team by Department of Education of Guangdong Province, China (2020KCXTD041), R&D Program of Beijing Municipal Education Commission (No. KM202110009013), Shanghai Pujiang Program (21PJ1402200).

语种:英文

外文关键词:industrial process monitoring; machine condition monitoring; AI; RTFD; industrial smart manufacturing

摘要:In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on "end-to-end" neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

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