Integrated system health management-oriented maintenance decision-making for multi-state system based on data mining

ABSTRACT

To ensure a series of missions can be completed with only finite breaks, many systems are required to guarantee system safety and mission success. Of these, maintenance decision support is vital. One widely used maintenance strategy has been selective maintenance. Most traditional selective maintenance optimisation research has focused on binary state systems, which are subject to distribution deterioration or failure. However, a majority of systems used in aerospace or industrial applications are multi-state systems with more than two states deteriorating at the same time, meaning that real-time state distribution is needed to provide more timely and effective maintenance. This paper presents a novel integrated system health management-oriented maintenance decision support methodology and framework for a multi-state system based on data mining. An aero-engine system numerical example is given to illustrate the methodology, the results of which demonstrate the significant advantages of using data mining to efficiently obtain state distribution information, and the benefits of using a robust optimal model to choose suitable strategies. This methodology, which is applicable to multi-state systems of varying sizes, has the ability to solve maintenance problems when imperfect maintenance quality is considered.

Keywords:

• Data mining
• integrated system health management
• multi-state system
• maintenance strategy

Acknowledgements

The authors are indebted to the editors and reviewers for the valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by a Program of National Natural Science Foundation of China#1 [grant number 71401136]; China Postdoctoral Science Foundation#2 [grant number 2014M552375].

Notes on contributors

Jiuping Xu

Jiuping Xu was born in 1962. He obtained his PhD (1995) from Tsinghua University and PhD (1999) from Sichuan University, respectively. He was elected the lifetime academician of the International Academy of Systems and Cybernetic Sciences. He is currently a professor of Sichuan University, the president of International Society for Management Science and Engineering Management, and the vice-president of The Systems Engineering Society of China. He has published over 40 books by Springer, Taylor & Francis, etc., and published over 400 journal papers in areas of uncertainty decision-making, space systems engineering, information system, etc.

Kai Sun

Kai Sun was born in 1988. He received his BS degrees in Tianjin University of Science and Technology. He is currently a PhD candidate at Sichuan University. His research interests include control theory, simulation, systems science, system engineering and information system.

Lei Xu

Lei Xu was born in 1982. He received his PhD degree in 2012 from Sichuan University and his BS and MS degrees from the University of Electronic Science and Technology of China (UESTC), in 2004 and 2007, respectively. He is currently an associate professor and the department head of electronics & business management of Xihua University, Chengdu, China, and a postdoctoral researcher at Uncertainty Decision-making Laboratory of Sichuan University, Chengdu, China. His area of research interest mainly focuses on systems engineering and risk management.