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Mathematical Problems in Engineering
Volume 2015, Article ID 793161, 17 pages
Review Article

Prognostics and Health Management: A Review on Data Driven Approaches

1Department of System Engineering and Engineering Management, City University of Hong Kong, Hong Kong
2Department of Industrial & Systems Engineering, National University of Singapore, Singapore 117576
3Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
4Faculty of Business and Law, Manchester Metropolitan University, Manchester M15 6BH, UK

Received 1 July 2014; Revised 25 October 2014; Accepted 31 October 2014

Academic Editor: Shaomin Wu

Copyright © 2015 Kwok L. Tsui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.