Methodologies for system-level remaining useful life prediction. (October 2016)

• Record Type: Journal Article
• Authors: Khorasgani, Hamed; Biswas, Gautam; Sankararaman, Shankar
• Is Part Of: Reliability engineering & system safety. Volume 154(2016:Oct.)
• Page Start: 8
• Page End: 18
• Publication Date: 2016-10
• Subjects: RUL remaining useful life -- EOL end of life -- PF particle filtering -- PDF probability density function -- CDF cumulative distribution function -- FOSM first-order second moment method -- FORM first-order reliability method -- PCs possible conflicts -- EKF extended Kalman filters -- LSE least squares estimation -- ARIMA auto regressive integrated moving average -- UT unscented transform -- SIR sampling importance resampling -- MPP most probable point; System-level prognostics -- Remaining useful life (RUL) -- Estimation and prediction algorithms -- Stochastic simulation -- Particle filters -- First order reliability method -- Uncertainty effects; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.05.006
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: While most prognostics approaches focus on accurate computation of the degradation rate and the remaining useful life (RUL) of individual components, it is the rate at which the performance of subsystems and systems degrade that is of greater interest to the operators and maintenance personnel of these systems. We develop a comprehensive methodology for system-level prognostics under different forms of uncertainty in this paper. Our approach combines an estimation scheme with a prediction scheme to compute the RUL as a stochastic distribution over the life of the system. We compare two prediction methods: (1) stochastic simulation and (2) the inverse first order reliability method (inverse-FORM). We compare the computational complexity and the accuracy of the two approaches using a case study of a system with several degrading components. Abstract : Highlights: Prognostics of systems with multiple degrading components are explored. The proposed system-level prognostics encompass estimation and prediction steps. A PF approach is developed for system state and parameter degradation estimations. Stochastic simulation and inverse-FORM are extended to system-level prediction. The computational complexity and the accuracy of the proposed methods are compared.