Condition-based monitoring as a robust strategy towards sustainable and resilient multi-energy infrastructure systems

被引:12
作者
Yodo, Nita [1 ]
Afrin, Tanzina [1 ]
Yadav, Om Prakash [1 ,2 ]
Wu, Di [3 ]
Huang, Ying [4 ]
机构
[1] North Dakota State Univ, Dept Ind & Mfg Engn, Fargo, ND 58105 USA
[2] North Carolina Agr & Tech State Univ, Dept Ind & Syst Engn, Greensboro, NC USA
[3] North Dakota State Univ, Dept Elect & Comp Engn, Fargo, ND USA
[4] North Dakota State Univ, Dept Civil Construct & Environm Engn, Fargo, ND USA
基金
美国国家科学基金会;
关键词
Resilience; sustainable; energy; infrastructure; condition-based monitoring; machine learning; VECTOR ENERGY NETWORKS; WIND TURBINE BEARING; OF-THE-ART; PREDICTIVE MAINTENANCE; FRAMEWORK; RECOVERY; RESTORATION; OPERATION; DIAGNOSIS; MACHINE;
D O I
10.1080/23789689.2022.2134648
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A resilient energy infrastructure system is exceptionally imperative to ensure uninterrupted energy supply to support the nation's economic growth. The resilience capability in energy infrastructures can be realized through effective planning decisions and maintenance strategies by implementing the condition-based monitoring (CBM) approach. CBM minimizes the unplanned downtime of a system by monitoring the system's health status in real-time and predicting upcoming failures. Thus, the planned maintenance can be performed before failures occur. With advancements in data analytics, conventional CBM methods have been enhanced with modern artificial intelligence algorithms to improve the prediction accuracy. This paper comprehensively evaluates the importance of CBM as a robust strategy to enhance energy infrastructure resilience. The vulnerabilities of energy infrastructure and current advancements in data-driven CBM methods are detailed. Furthermore, this survey equip energy infrastructure stakeholders and practitioners with CBM knowledge in managing unforeseen disaster risks, such as power failures due to adverse weather conditions.
引用
收藏
页码:170 / 189
页数:20
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