A generalized remaining useful life prediction method for complex systems based on composite health indicator

• Published in: Reliability engineering & system safety Vol. 205; p. 107241
• Main Authors: Wen, Pengfei, Zhao, Shuai, Chen, Shaowei, Li, Yong
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.01.2021; Elsevier BV
• Subjects: Aircraft engines; Complex systems; Condition monitoring; Confidence intervals; Data fusion; Data integration; Degradation; Degradation modeling; Downtime; Genetic algorithms; Life prediction; Multiple sensors; Predictions; Prognostics; Reliability engineering; Remaining useful life; Signal monitoring; Turbine engines; Turbines; Useful life; Data fusion; Multiple sensors; Remaining useful life; Prognostics; Degradation modeling
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2020.107241

•A nonlinear data fusion method is proposed for the composite health indicator.•The data fusion method is directly performed in terms of the RUL prediction model.•The reliability measures are explicitly derived and computationally efficient.•The superiority of the method is well verified with NASA dataset C-MAPSS. As one of the key techniques in Prognostics and Health Management (PHM), accurate Remaining Useful Life (RUL) prediction can effectively reduce the number of downtime maintenance and significantly improve economic benefits. In this paper, a generalized RUL prediction method is proposed for complex systems with multiple Condition Monitoring (CM) signals. A stochastic degradation model is proposed to characterize the system degradation behavior, based on which the respective reliability characteristics such as the RUL and its Confidence Interval (CI) are explicitly derived. Considering the degradation model, two desirable properties of the Health Indicator (HI) are put forward and their respective quantitative evaluation methods are developed. With the desirable properties, a nonlinear data fusion method based on Genetic Programming (GP) is proposed to construct a superior composite HI. In this way, the multiple CM signals are fused to provide a better prediction capability. Finally, the proposed integrated methodology is validated on the C-MAPSS data set of aircraft turbine engines.