A Generalized Remaining Useful Life Prediction Method for Complex Systems Based on Composite Health Indicator

Pengfei Wen, Shuai Zhao*, Shaowei Chen, Yong Li

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number107241
JournalReliability Engineering & System Safety
Volume205
ISSN0951-8320
DOIs
Publication statusPublished - 2021

Keywords

  • Data fusion
  • Degradation modeling
  • Multiple sensors
  • Prognostics
  • Remaining useful life

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