Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (2): 530-542.doi: 10.23919/JSEE.2023.000042

• RELIABILITY • Previous Articles    

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Fengfei WANG(), Shengjin TANG(), Liang LI(), Xiaoyan SUN(), Chuanqiang YU(), Xiaosheng SI()   

  1. 1 College of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China
  • Received:2021-09-27 Online:2023-04-18 Published:2023-04-18
  • Contact: Shengjin TANG E-mail:18755187114@163.com;tangshengjin27@126.com;xzj_921@163.com;sunxiaoyantsj@126.com;fishychq@163.com;sixiaosheng@gmail.com
  • About author:
    WANG Fengfei was born in 1997. He received his B.S. degree from Hefei University of Technology in 2019 and M.S. degree from Rocket Force University of Engineering in 2021. Now, he is currently pursuing his Ph.D. degree in Rocket Force University of Engineering. His main research interests are prognostics and health management, remaining useful life prediction and reliability assessment. E-mail: 18755187114@163.com

    TANG Shengjin was born in 1985. He received his B.S., M.S. and Ph.D. degrees in 2007, 2010 and 2015 from Rocket Force University of Engineering, Xi ’an, China. He is currently an associate professor with Rocket Force University of Engineering, Xi’an, China. His main research interests include prognostics and health management, reliability assessment, predictive maintenance, and image processing. E-mail: tangshengjin27@126.com

    LI Liang was born in 1984. He received his B.S., M.S. and Ph.D. degrees from Rocket Force University of Engineering, Xi ’an, China. He is currently a lecturer with Rocket Force University of Engineering, Xi ’an, China. His main research interests are fault diagnosis of hydraulic system, and hydraulic control theory and engineering. E-mail: xzj_921@163.com

    SUN Xiaoyan was born in 1987. She received her B.S. degree in 2010 from Southeast University Chengxian College, Nanjing, China, and M.S. degree from Xi ’an Technological University, Xi’an, China, in 2013. She is currently a lecturer with Rocket Force University of Engineering, Xi ’an, China. Her main research interests include prognostics and health management, image processing, and machine learning E-mail: sunxiaoyantsj@126.com

    YU Chuanqiang was born in 1975. He received his B.S., M.S., and Ph.D. degrees from Rocket Force University of Engineering, Xi’an, China, in 2000, 2003, and 2007, respectively. He is currently a professor with Rocket Force University of Engineering, Xi ’an, China. His main research interests are reliability assessment, driverless cars and image processing. E-mail: fishychq@163.com

    SI Xiaosheng was born in 1984. He received his B.S., M.S., and Ph.D. degrees from Rocket Force University of Engineering, Xi’an, China, in 2006, 2009, and 2014, respectively. He is currently a professor with Rocket Force University of Engineering. His research interests include evidence theory, expert system, prognostics and health management, reliability assessment, predictive maintenance and lifetime estimation. E-mail: sixiaosheng@gmail.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61703410;61873175;62073336;61873273;61773386;61922089) and the Basic Research Plan of Shaanxi Natural Science Foundation of China (2022JM-376)

Abstract:

Remaining useful life (RUL) prediction is one of the most crucial components in prognostics and health management (PHM) of aero-engines. This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold. Firstly, a random-coefficient regression (RCR) model is used to model the degradation process of aero-engines. Then, the RUL distribution based on fixed failure threshold is derived. The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation (MLE) method and the random coefficient is updated in real time under the Bayesian framework. The failure threshold in this paper is defined by the actual degradation process of aero-engines. After that, a expectation maximization (EM) algorithm is proposed to estimate the underlying failure threshold of aero-engines. In addition, the conditional probability is used to satisfy the limitation of failure threshold. Then, based on above results, an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold (RFT) is derived in a closed-form. Finally, a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

Key words: aero-engine, remaining useful life (RUL), random failure threshold (RFT), random-coefficient regression (RCR), parameters estimation