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

abstract

• 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.

authors

• Elwany, Alaa Mohamed

published proceedings

• IIE TRANSACTIONS

author list (cited authors)

• Elwany, A. H., & Gebraeel, N. Z.

citation count

• 171

complete list of authors

• Elwany, Alaa H||Gebraeel, Nagi Z

publication date

• January 2008

publisher

• Taylor & Francis  Publisher

published in

• IIE Transactions  Journal

keywords

• Condition Monitoring
• Degradation Modeling
• Replacement Models
• Spare Parts Inventory

Digital Object Identifier (DOI)

• 10.1080/07408170701730818

start page

• 629

end page

• 639

volume

• 40

issue

• 7

URL

• http://dx.doi.org/10.1080/07408170701730818