Big Data Opportunities: System Health Monitoring and Management

被引:11
|
作者
Tsui, Kwok Leung [1 ,2 ]
Zhao, Yang [1 ,2 ]
Wang, Dong [3 ]
机构
[1] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Ctr Syst Informat Engn, Hong Kong, Peoples R China
[3] Shanghai Jiao Tong Univ, Dept Ind Engn & Management, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Active and passive data; big data; complex systems; system health monitoring and management; LITHIUM-ION BATTERY; USEFUL LIFE PREDICTION; SURVEILLANCE SYSTEM; DEGRADATION SIGNALS; CHARGE ESTIMATION; PROGNOSTICS; STATE; DIAGNOSTICS; CHALLENGES; DISEASE;
D O I
10.1109/ACCESS.2019.2917891
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The concept of a system, generally defined as an organized set of detailed methods, procedures, and routines that are created to carry out a specific activity or solve a specific problem, has been successfully applied to many domains, ranging from mechanical systems to public health. System health monitoring and management (SHMM) refers to the framework of continuous surveillance, analysis, and interpretation of relevant data for system maintenance, management, and strategic planning. This framework is essential to ensure that an entire system is stable and under control. A fundamental problem in SHMM is the optimal use of correlated active and passive data in tasks including prediction and forecasting, monitoring and surveillance, fault detection and diagnostics, engineering management, and supply chain management. In this paper, we provide a new perspective on SHMM in a big data environment, discuss its relationship with other disciplines, and present several of its applications to complex systems.
引用
收藏
页码:68853 / 68867
页数:15
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