Optimal replacement of continuously degrading systems in partially observed environments
ABSTRACT Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the co...
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Published in | Naval research logistics Vol. 62; no. 5; pp. 395 - 415 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Blackwell Publishing Ltd
01.08.2015
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Abstract | ABSTRACT
Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the component fails when it is accumulated degradation first reaches a fixed threshold. Assuming periodic inspection of the component, the objective is to minimize the long‐run average cost per unit time of performing preventive and reactive replacements for two distinct cases. The first case examines instantaneous replacements and fixed costs, while the second considers time‐consuming replacements and revenue losses accrued during periods of unavailability. Formulated and solved are mixed state space, partially observable Markov decision process models, both of which reveal the optimality of environment‐dependent threshold policies with respect to the component's cumulative degradation level. Additionally, it is shown that for each degradation value, a threshold policy with respect to the environment belief state is optimal if the environment alternates between two states. The threshold policies are illustrated by way of numerical examples using both synthetic and real wind turbine data. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 395–415, 2015 |
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AbstractList | Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the component fails when it is accumulated degradation first reaches a fixed threshold. Assuming periodic inspection of the component, the objective is to minimize the long‐run average cost per unit time of performing preventive and reactive replacements for two distinct cases. The first case examines instantaneous replacements and fixed costs, while the second considers time‐consuming replacements and revenue losses accrued during periods of unavailability. Formulated and solved are mixed state space, partially observable Markov decision process models, both of which reveal the optimality of environment‐dependent threshold policies with respect to the component's cumulative degradation level. Additionally, it is shown that for each degradation value, a threshold policy with respect to the environment belief state is optimal if the environment alternates between two states. The threshold policies are illustrated by way of numerical examples using both synthetic and real wind turbine data. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 395–415, 2015 Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the component fails when it is accumulated degradation first reaches a fixed threshold. Assuming periodic inspection of the component, the objective is to minimize the long-run average cost per unit time of performing preventive and reactive replacements for two distinct cases. The first case examines instantaneous replacements and fixed costs, while the second considers time-consuming replacements and revenue losses accrued during periods of unavailability. Formulated and solved are mixed state space, partially observable Markov decision process models, both of which reveal the optimality of environment-dependent threshold policies with respect to the component's cumulative degradation level. Additionally, it is shown that for each degradation value, a threshold policy with respect to the environment belief state is optimal if the environment alternates between two states. The threshold policies are illustrated by way of numerical examples using both synthetic and real wind turbine data. Naval Research Logistics 62: 395-415, 2015 ABSTRACT Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the component fails when it is accumulated degradation first reaches a fixed threshold. Assuming periodic inspection of the component, the objective is to minimize the long‐run average cost per unit time of performing preventive and reactive replacements for two distinct cases. The first case examines instantaneous replacements and fixed costs, while the second considers time‐consuming replacements and revenue losses accrued during periods of unavailability. Formulated and solved are mixed state space, partially observable Markov decision process models, both of which reveal the optimality of environment‐dependent threshold policies with respect to the component's cumulative degradation level. Additionally, it is shown that for each degradation value, a threshold policy with respect to the environment belief state is optimal if the environment alternates between two states. The threshold policies are illustrated by way of numerical examples using both synthetic and real wind turbine data. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 395–415, 2015 |
Author | Kharoufeh, Jeffrey P. Flory, John A. Abdul-Malak, David T. |
Author_xml | – sequence: 1 givenname: John A. surname: Flory fullname: Flory, John A. organization: Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall 3700 O'Hara Street, Pennsylvania, 15261, Pittsburgh – sequence: 2 givenname: Jeffrey P. surname: Kharoufeh fullname: Kharoufeh, Jeffrey P. email: jkharouf@pitt.edu organization: Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall 3700 O'Hara Street, Pennsylvania, 15261, Pittsburgh – sequence: 3 givenname: David T. surname: Abdul-Malak fullname: Abdul-Malak, David T. organization: Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall 3700 O'Hara Street, Pennsylvania, 15261, Pittsburgh |
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Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates... Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a... |
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SubjectTerms | Degradation Logistics Naval optimal replacement Optimization Policies random environment Thresholds Wind energy wind turbine Wind turbines |
Title | Optimal replacement of continuously degrading systems in partially observed environments |
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