A maintenance decision-making method based on stochastic processes and evidential variables considering small sample conditions.
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| Title: | A maintenance decision-making method based on stochastic processes and evidential variables considering small sample conditions. |
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| Authors: | Duan, Xiaochuan1 (AUTHOR), Wang, Shaoping1 (AUTHOR), Shi, Jian1 (AUTHOR), Yang, Zhou2 (AUTHOR), Liu, Di1 (AUTHOR) liudi54834@buaa.edu.cn |
| Source: | International Journal of Production Research. May2026, Vol. 64 Issue 9, p3336-3351. 16p. |
| Subjects: | Stochastic processes, Decision making, Reciprocating pumps, Statistical models, Mathematical variables, Maintenance costs |
| Abstract: | Maintenance is crucial for increasing the reliability of a system during its usage. In this paper, a maintenance decision-making optimisation method, based on stochastic processes and evidential variables, is proposed. This method reduces the high maintenance costs caused by the inability to accurately evaluate the remaining useful life (RUL) of the underlying equipment under small sample sizes when based on random variables. A degradation model is first developed based on stochastic processes supported by evidential variables, the RUL interval of the equipment and its corresponding Basic Probability Assignments (BPAs) are accurately predicted. A maintenance decision-making model is then developed based on evidential variables, where the expected maintenance cost per unit time is considered as the objective function while the ordering and maintenance times of spare parts are considered as decision variables. Finally, the objective function is optimised to determine the optimal ordering and maintenance times. Considering the piston pump as an example, it is demonstrated that the proposed method has high effectiveness in predicting the RUL and making maintenance decisions. It reduces the expected maintenance cost per unit time by 0.1054- 2.5171%. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193388862 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A maintenance decision-making method based on stochastic processes and evidential variables considering small sample conditions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Duan%2C+Xiaochuan%22">Duan, Xiaochuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Shaoping%22">Wang, Shaoping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Jian%22">Shi, Jian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Zhou%22">Yang, Zhou</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Di%22">Liu, Di</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liudi54834@buaa.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. May2026, Vol. 64 Issue 9, p3336-3351. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Reciprocating+pumps%22">Reciprocating pumps</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+variables%22">Mathematical variables</searchLink><br /><searchLink fieldCode="DE" term="%22Maintenance+costs%22">Maintenance costs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Maintenance is crucial for increasing the reliability of a system during its usage. In this paper, a maintenance decision-making optimisation method, based on stochastic processes and evidential variables, is proposed. This method reduces the high maintenance costs caused by the inability to accurately evaluate the remaining useful life (RUL) of the underlying equipment under small sample sizes when based on random variables. A degradation model is first developed based on stochastic processes supported by evidential variables, the RUL interval of the equipment and its corresponding Basic Probability Assignments (BPAs) are accurately predicted. A maintenance decision-making model is then developed based on evidential variables, where the expected maintenance cost per unit time is considered as the objective function while the ordering and maintenance times of spare parts are considered as decision variables. Finally, the objective function is optimised to determine the optimal ordering and maintenance times. Considering the piston pump as an example, it is demonstrated that the proposed method has high effectiveness in predicting the RUL and making maintenance decisions. It reduces the expected maintenance cost per unit time by 0.1054- 2.5171%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2025.2594084 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 3336 Subjects: – SubjectFull: Stochastic processes Type: general – SubjectFull: Decision making Type: general – SubjectFull: Reciprocating pumps Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Mathematical variables Type: general – SubjectFull: Maintenance costs Type: general Titles: – TitleFull: A maintenance decision-making method based on stochastic processes and evidential variables considering small sample conditions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Duan, Xiaochuan – PersonEntity: Name: NameFull: Wang, Shaoping – PersonEntity: Name: NameFull: Shi, Jian – PersonEntity: Name: NameFull: Yang, Zhou – PersonEntity: Name: NameFull: Liu, Di IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 64 – Type: issue Value: 9 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
