Abstract
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.
| Original language | English |
|---|---|
| Article number | 8718609 |
| Pages (from-to) | 68853-68867 |
| Journal | IEEE Access |
| Volume | 7 |
| Online published | 20 May 2019 |
| DOIs | |
| Publication status | Published - 2019 |
Research Keywords
- Active and passive data
- big data
- complex systems
- system health monitoring and management
Publisher's Copyright Statement
- COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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