 Methodologies for system-level remaining useful life predictionHamed Khorasgani, Gautam Biswas and Shankar Sankararaman Reliability Engineering and System Safety, 2016, vol. 154, issue C, 8-18 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. Keywords: System-level prognostics; Remaining useful life (RUL); Estimation and prediction algorithms; Stochastic simulation; Particle filters; First order reliability method; Uncertainty effects (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:154:y:2016:i:c:p:8-18 DOI: 10.1016/j.ress.2016.05.006 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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