Structured Replacement Policies for Components with Complex Degradation Processes and Dedicated Sensors

Alaa H. Elwany (), Nagi Z. Gebraeel () and Lisa M. Maillart ()
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Alaa H. Elwany: Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Nagi Z. Gebraeel: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Lisa M. Maillart: Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260

Operations Research, 2011, vol. 59, issue 3, 684-695

Abstract: Failure of many engineering systems usually results from a gradual and irreversible accumulation of damage, a degradation process. Most degradation processes can be monitored using sensor technology. The resulting degradation signals are usually correlated with the degradation process. A system is considered to have failed once its degradation signal reaches a prespecified failure threshold. This paper considers a replacement problem for components whose degradation process can be monitored using dedicated sensors. First, we present a stochastic degradation modeling framework that characterizes, in real time, the path of a component's degradation signal. These signals are used to predict the evolution of the component's degradation state. Next, we formulate a single-unit replacement problem as a Markov decision process and utilize the real-time signal observations to determine a replacement policy. We focus on exponentially increasing degradation signals and show that the optimal replacement policy for this class of problems is a monotonically nondecreasing control limit policy. Finally, the model is used to determine an optimal replacement policy by utilizing vibration-based degradation signals from a rotating machinery application.

Keywords: Markov decision processes; control limit policies; single-unit replacement model; degradation modeling; nonstationary degradation; sensors (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (47)

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