Industrial big data-driven fault prognostics and health management

被引：0

作者：

Jin, Xiaohang ^{[1
,2
]}

Wang, Yu ^{[3
]}

Zhang, Bin ^{[4
]}

机构：

[1] College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou,310023, China

[2] Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou,310023, China

[3] State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an,710054, China

[4] Department of Electrical Engineering, University of South Carolina, Columbia,SC,29208, United States

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 05期

关键词：

Artificial intelligence technologies - Data driven - Development and applications - Economic values - Fault prognostics - Hard Disk Drive - Health management technologies - Industrial big data - Prognostic and health management - Technology equipment;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

With the development and application of artificial intelligence technology, equipment has accumulated massive amount of industrial big data, which pushed the equipment Prognostics and Health Management (PHM) technology into the era of industrial big data. There had great economic and social value to extract useful information in industrial big data for PHM by combining with the function, structure and working characteristics of the equipment. The development and application of PHM technology were reviewed, and the industrial big data analysis methods were discussed. Two case studies of unity-scale wind turbines and hard disk drives in big data environments were presented to demonstrate the advantages of industrial big data-driven PHM, which could provide a reference for researchers in related fields. © 2022, Editorial Department of CIMS. All right reserved.

引用

页码：1314 / 1336

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