IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i3d10.1007_s10845-021-01855-3.html
   My bibliography  Save this article

An ontology model for maintenance strategy selection and assessment

Author

Listed:
  • Juan José Montero Jiménez

    (ISAE-SUPAERO, Université de Toulouse
    TEC-Tecnológico de Costa Rica)

  • Rob Vingerhoeds

    (ISAE-SUPAERO, Université de Toulouse)

  • Bernard Grabot

    (ENIT-INP Toulouse)

  • Sébastien Schwartz

    (ISAE-SUPAERO, Université de Toulouse)

Abstract

Within maintenance management activities, engineers need to select maintenance strategies so to carry out the technical maintenance actions. A single equipment is composed of several components with different failure modes. There should be a maintenance strategy for each of them; while some of the components can be run-to-failure applying corrective maintenance, some others cannot afford a failure, and preventive or predictive strategies should be implemented. Selecting and assessing maintenance strategies is a complex task for which information from many sources should be retrieved. Information from a Failure Mode, Effects and Criticality Analysis, a cost–benefit-risk analysis, Computational Maintenance Management Systems, is often used by engineers to select and assess maintenance strategies. A selected strategy is often not evaluated over time to check its effectiveness. The strategy may need adjustments or substituted by a more efficient one, for example, a condition-based strategy substituting a time-based one. To facilitate maintenance strategies selection and assessment, the current study proposes an Ontology model for Maintenance Strategy Selection and Assessment (OMSSA). OMSSA serves as a formal terminology framework in maintenance strategies that can be used to develop smart computational agents that can help in the decision-making process for selecting and assessing maintenance strategies. To facilitate its future reuse and integration with other ontologies in the industrial domain, OMSSA builds following the state-of-the-art in ontology development by using a top-level domain-neutral ontology, the Basic Formal Ontology.

Suggested Citation

  • Juan José Montero Jiménez & Rob Vingerhoeds & Bernard Grabot & Sébastien Schwartz, 2023. "An ontology model for maintenance strategy selection and assessment," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1369-1387, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01855-3
    DOI: 10.1007/s10845-021-01855-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01855-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01855-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Asma Talhi & Virginie Fortineau & Jean-Charles Huet & Samir Lamouri, 2019. "Ontology for cloud manufacturing based Product Lifecycle Management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2171-2192, June.
    2. Ikuobase Emovon & Rosemary A. Norman & Alan J. Murphy, 2018. "Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 519-531, March.
    3. Heravi, Bahareh Rahmanzadeh & Lycett, Mark & de Cesare, Sergio, 2014. "Ontology-based standards development: Application of OntoStanD to ebXML business process specification schema," International Journal of Accounting Information Systems, Elsevier, vol. 15(3), pages 275-297.
    4. Yuqian Lu & Hongqiang Wang & Xun Xu, 2019. "ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 317-334, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    2. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.
    3. Reza Vatankhah Barenji, 2022. "A blockchain technology based trust system for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1451-1465, June.
    4. Jiatong Yu & Jiajue Wang & Taesoo Moon, 2022. "Influence of Digital Transformation Capability on Operational Performance," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    5. Shiyong Yin & Jinsong Bao & Jie Zhang & Jie Li & Junliang Wang & Xiaodi Huang, 2020. "Real-time task processing for spinning cyber-physical production systems based on edge computing," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2069-2087, December.
    6. Shashi Bhushan Jha & Radu F. Babiceanu & Remzi Seker, 2020. "Formal modeling of cyber-physical resource scheduling in IIoT cloud environments," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1149-1164, June.
    7. Athanasios P. Vavatsikos & Kalliopi F. Sotiropoulou & Veniamin Tzingizis, 2022. "GIS-assisted suitability analysis combining PROMETHEE II, analytic hierarchy process and inverse distance weighting," Operational Research, Springer, vol. 22(5), pages 5983-6006, November.
    8. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    9. Jiatong Yu & Taesoo Moon, 2021. "Impact of Digital Strategic Orientation on Organizational Performance through Digital Competence," Sustainability, MDPI, vol. 13(17), pages 1-15, August.
    10. Liu, Aijun & Zhao, Yingxue & Meng, Xiaoge & Zhang, Yan, 2020. "A three-phase fuzzy multi-criteria decision model for charging station location of the sharing electric vehicle," International Journal of Production Economics, Elsevier, vol. 225(C).
    11. Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.
    12. Dohale, Vishwas & Gunasekaran, Angappa & Akarte, Milind & Verma, Priyanka, 2021. "An integrated Delphi-MCDM-Bayesian Network framework for production system selection," International Journal of Production Economics, Elsevier, vol. 242(C).
    13. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01855-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.