Prognostics and Health Management: A Review on Data Driven Approaches

被引:253
作者
Tsui, Kwok L. [1 ]
Chen, Nan [2 ]
Zhou, Qiang [1 ]
Hai, Yizhen [1 ]
Wang, Wenbin [3 ,4 ]
机构
[1] City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
[2] Natl Univ Singapore, Dept Ind & Syst Engn, Singapore 117576, Singapore
[3] Univ Sci & Technol Beijing, Donlinks Sch Econ & Management, Beijing 100083, Peoples R China
[4] Manchester Metropolitan Univ, Fac Business & Law, Manchester M15 6BH, Lancs, England
基金
中国博士后科学基金;
关键词
CONDITION-BASED MAINTENANCE; REMAINING USEFUL LIFE; INVERSE GAUSSIAN PROCESS; GEARBOX FAULT-DETECTION; FEATURE-EXTRACTION; MONITORING INTERVALS; DEGRADATION SIGNALS; OPTIMAL REPLACEMENT; WIENER-PROCESSES; PROCESS MODEL;
D O I
10.1155/2015/793161
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.
引用
收藏
页数:17
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