Sensor-driven prognostic models for equipment replacement and spare parts inventory

A.M.H. Elwany, N.Z. Gebraeel

Research output: Contribution to journalArticleAcademicpeer-review

177 Citations (Scopus)
4 Downloads (Pure)


Accurate predictions of equipment failure times are necessary to improve replacement and spare parts inventory decisions. Most of the existing decision models focus on using population-specific reliability characteristics, such as failure time distributions, to develop decision-making strategies. Since these distributions are unaffected by the underlying physical degradation processes, they do not distinguish between the different degradation characteristics of individual components of the population. This results in less accurate failure predictability and hence less accurate replacement and inventory decisions. In this paper, we develop a sensor-driven decision model for component replacement and spare parts inventory. We integrate a degradation modeling framework for computing remaining life distributions using condition-based in situ sensor data with existing replacement and inventory decision models. This enables the dynamic updating of replacement and inventory decisions based on the physical condition of the equipment.
Original languageEnglish
Pages (from-to)629-639
JournalIIE Transactions
Issue number7
Publication statusPublished - 2008


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