Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

A. Mancuso; M. Compare; A. Salo; E. Zio

doi:10.1016/j.ress.2021.107536

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

A. Mancuso^*, M. Compare, A. Salo, E. Zio

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

52 Citations (Scopus)

104 Downloads (Pure)

Abstract

The performance of the Prognostics and Health Management (PHM) depends both on the functioning of the measurement acquisition system and on the actual state of the system being monitored. The dependencies between these systems must be considered when developing optimal inspection and maintenance strategies. This paper presents a methodology to support the definition maintenance strategies for PHM-equipped industrial systems. The methodology employs influence diagrams when seeking to maximize the expected utility of system operation. The optimization problem is solved by mixed-integer linear programming, subject to budget and technical constraints. Chance constraints can be also included, for instance to curtail risks based on measures such as the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of system operation. The viability of the methodology is demonstrated by optimizing the inspection and maintenance strategy for a gas turbine equipped with PHM solution. The computation of the Value of Perfect Information (VoPI) provides additional insights on maintenance management.

Original language	English
Article number	107536
Number of pages	10
Journal	Reliability Engineering and System Safety
Volume	210
DOIs	https://doi.org/10.1016/j.ress.2021.107536
Publication status	Published - Jun 2021
MoE publication type	A1 Journal article-refereed

Funding

The research has been supported by the project Platform Value Now, funded by the Strategic Research Council of the Academy of Finland (grant number 314207 ) . The case study has been performed using Julia Programming language with the technical support of M.Sc. Juho Andelmin.

Keywords

Decision Programming
Gas turbine
Influence diagrams
Predictive maintenance
Prognostics and Health Management
Value of Perfect Information

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10.1016/j.ress.2021.107536Licence: CC BY

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systemsFinal published version, 507 KBLicence: CC BY

Cite this

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title = "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems",

abstract = "The performance of the Prognostics and Health Management (PHM) depends both on the functioning of the measurement acquisition system and on the actual state of the system being monitored. The dependencies between these systems must be considered when developing optimal inspection and maintenance strategies. This paper presents a methodology to support the definition maintenance strategies for PHM-equipped industrial systems. The methodology employs influence diagrams when seeking to maximize the expected utility of system operation. The optimization problem is solved by mixed-integer linear programming, subject to budget and technical constraints. Chance constraints can be also included, for instance to curtail risks based on measures such as the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of system operation. The viability of the methodology is demonstrated by optimizing the inspection and maintenance strategy for a gas turbine equipped with PHM solution. The computation of the Value of Perfect Information (VoPI) provides additional insights on maintenance management.",

keywords = "Decision Programming, Gas turbine, Influence diagrams, Predictive maintenance, Prognostics and Health Management, Value of Perfect Information",

author = "A. Mancuso and M. Compare and A. Salo and E. Zio",

note = "Funding Information: The research has been supported by the project Platform Value Now, funded by the Strategic Research Council of the Academy of Finland (grant number 314207 ) . The case study has been performed using Julia Programming language with the technical support of M.Sc. Juho Andelmin. Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

year = "2021",

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doi = "10.1016/j.ress.2021.107536",

language = "English",

volume = "210",

journal = "Reliability Engineering and System Safety",

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AU - Mancuso, A.

AU - Compare, M.

AU - Salo, A.

AU - Zio, E.

N1 - Funding Information: The research has been supported by the project Platform Value Now, funded by the Strategic Research Council of the Academy of Finland (grant number 314207 ) . The case study has been performed using Julia Programming language with the technical support of M.Sc. Juho Andelmin. Publisher Copyright: © 2021 Elsevier Ltd

PY - 2021/6

Y1 - 2021/6

N2 - The performance of the Prognostics and Health Management (PHM) depends both on the functioning of the measurement acquisition system and on the actual state of the system being monitored. The dependencies between these systems must be considered when developing optimal inspection and maintenance strategies. This paper presents a methodology to support the definition maintenance strategies for PHM-equipped industrial systems. The methodology employs influence diagrams when seeking to maximize the expected utility of system operation. The optimization problem is solved by mixed-integer linear programming, subject to budget and technical constraints. Chance constraints can be also included, for instance to curtail risks based on measures such as the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of system operation. The viability of the methodology is demonstrated by optimizing the inspection and maintenance strategy for a gas turbine equipped with PHM solution. The computation of the Value of Perfect Information (VoPI) provides additional insights on maintenance management.

AB - The performance of the Prognostics and Health Management (PHM) depends both on the functioning of the measurement acquisition system and on the actual state of the system being monitored. The dependencies between these systems must be considered when developing optimal inspection and maintenance strategies. This paper presents a methodology to support the definition maintenance strategies for PHM-equipped industrial systems. The methodology employs influence diagrams when seeking to maximize the expected utility of system operation. The optimization problem is solved by mixed-integer linear programming, subject to budget and technical constraints. Chance constraints can be also included, for instance to curtail risks based on measures such as the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of system operation. The viability of the methodology is demonstrated by optimizing the inspection and maintenance strategy for a gas turbine equipped with PHM solution. The computation of the Value of Perfect Information (VoPI) provides additional insights on maintenance management.

KW - Decision Programming

KW - Gas turbine

KW - Influence diagrams

KW - Predictive maintenance

KW - Prognostics and Health Management

KW - Value of Perfect Information

UR - https://www.scopus.com/pages/publications/85101384831

U2 - 10.1016/j.ress.2021.107536

DO - 10.1016/j.ress.2021.107536

M3 - Article

AN - SCOPUS:85101384831

SN - 0951-8320

VL - 210

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

M1 - 107536

ER -

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Abstract

Funding

Keywords

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Platform Value Now: Value capturing in the fast emerging platform ecosystems

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Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Abstract

Funding

Keywords

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Platform Value Now: Value capturing in the fast emerging platform ecosystems

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