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Similarity-based information fusion grey model for remaining useful life prediction of aircraft engines

Xiaoyu Yang (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Zhigeng Fang (Institute for Grey Systems Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Xiaochuan Li (Institute of Artificial Intelligence, De Montfort University, Leicester, UK)
Yingjie Yang (De Montfort University, Leicester, UK)
David Mba (De Montfort University, Leicester, UK)

Grey Systems: Theory and Application

ISSN: 2043-9377

Article publication date: 16 October 2020

Issue publication date: 18 June 2021




Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.


First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.


The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

Research limitations/implications

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

Practical implications

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.


This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.



This work was supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18_0233), National Natural Science Foundation of China (No.71671091, 71811530338), the China Scholarship fund, the Fundamental Research Funds for Central Universities (NP2019104, NC2019003), the Leverhulme Trust International Research Network project (IN-2014-020) and the Royal Society and NSFC International Exchanges project (IEC\NSFC\170391).


Yang, X., Fang, Z., Li, X., Yang, Y. and Mba, D. (2021), "Similarity-based information fusion grey model for remaining useful life prediction of aircraft engines", Grey Systems: Theory and Application, Vol. 11 No. 3, pp. 463-483.



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