TY - JOUR
T1 - A Generalized Remaining Useful Life Prediction Method for Complex Systems Based on Composite Health Indicator
AU - Wen, Pengfei
AU - Zhao, Shuai
AU - Chen, Shaowei
AU - Li, Yong
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Data fusion
KW - Degradation modeling
KW - Multiple sensors
KW - Prognostics
KW - Remaining useful life
UR - http://www.scopus.com/inward/record.url?scp=85091526907&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2020.107241
DO - 10.1016/j.ress.2020.107241
M3 - Journal article
SN - 0951-8320
VL - 205
JO - Reliability Engineering & System Safety
JF - Reliability Engineering & System Safety
M1 - 107241
ER -