IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v180y2018icp136-151.html
   My bibliography  Save this article

A holistic multi-failure mode prognosis approach for complex equipment

Author

Listed:
  • Blancke, Olivier
  • Tahan, Antoine
  • Komljenovic, Dragan
  • Amyot, Normand
  • Lévesque, Mélanie
  • Hudon, Claude

Abstract

The aim of this paper is to propose a holistic multi-failure mode prognosis approach that takes into account the complexity of failure mechanisms as a system. Model assumptions are first proposed by experts and then formalized using graph theory and stochastic models. The prognosis approach relies on a diagnostic algorithm that combines diagnostic information from different sources (e.g., measurements and inspections) to detect active failure mechanisms and track their progression, and a prognostic algorithm that predicts failure mode occurrences dynamically as new information becomes available. Furthermore, the approach identifies undetectable failure mechanisms where no symptoms have yet been measured. The relative simplicity of the algorithms and graphical representation of the results helps to build decision-makers’ trust. In addition, the approach is a means of capturing acquired knowledge and available data. A case study of a hydroelectric generator stator is proposed. The resulting multi-state degradation model identified more than 150 failure mechanisms discretized in 70 physical states and leading to three failure modes. Three historical failure and one online case studies are presented, based on diagnostic data from Hydro-Québec's generating fleet. In two of the case studies, the failure mode occurrence could have been predicted more than eight years in advance.

Suggested Citation

  • Blancke, Olivier & Tahan, Antoine & Komljenovic, Dragan & Amyot, Normand & Lévesque, Mélanie & Hudon, Claude, 2018. "A holistic multi-failure mode prognosis approach for complex equipment," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 136-151.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:136-151
    DOI: 10.1016/j.ress.2018.07.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832017312632
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2018.07.006?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. Khorasgani, Hamed & Biswas, Gautam & Sankararaman, Shankar, 2016. "Methodologies for system-level remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 8-18.
    2. Li, Ying Yi & Chen, Ying & Yuan, Zeng Hui & Tang, Ning & Kang, Rui, 2017. "Reliability analysis of multi-state systems subject to failure mechanism dependence based on a combination method," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 109-123.
    3. Chiachío, Juan & Chiachío, Manuel & Sankararaman, Shankar & Saxena, Abhinav & Goebel, Kai, 2015. "Condition-based prediction of time-dependent reliability in composites," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 134-147.
    4. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    5. Wang, Wei & Maio, Francesco Di & Zio, Enrico, 2017. "Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 276-289.
    6. Chiachío, Manuel & Chiachío, Juan & Sankararaman, Shankar & Goebel, Kai & Andrews, John, 2017. "A new algorithm for prognostics using Subset Simulation," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 189-199.
    7. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & Mc Donnell, D., 2017. "Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 25-40.
    8. Aven, Terje & Zio, Enrico, 2011. "Some considerations on the treatment of uncertainties in risk assessment for practical decision making," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 64-74.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moradi, Ramin & Groth, Katrina M., 2020. "Modernizing risk assessment: A systematic integration of PRA and PHM techniques," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Li, Ruopu & Arzaghi, Ehsan & Abbassi, Rouzbeh & Chen, Diyi & Li, Chunhao & Li, Huanhuan & Xu, Beibei, 2020. "Dynamic maintenance planning of a hydro-turbine in operational life cycle," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Mohamed Gaha & Bilal Chabane & Dragan Komljenovic & Alain Côté & Claude Hébert & Olivier Blancke & Atieh Delavari & Georges Abdul-Nour, 2021. "Global Methodology for Electrical Utilities Maintenance Assessment Based on Risk-Informed Decision Making," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    5. Barbosa de Santis, Rodrigo & Silveira Gontijo, Tiago & Azevedo Costa, Marcelo, 2021. "Condition-based maintenance in hydroelectric plants: A systematic literature review," MPRA Paper 115912, University Library of Munich, Germany.

    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. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    2. Chiachío, Juan & Jalón, María L. & Chiachío, Manuel & Kolios, Athanasios, 2020. "A Markov chains prognostics framework for complex degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    3. Tosoni, E. & Salo, A. & Govaerts, J. & Zio, E., 2019. "Comprehensiveness of scenarios in the safety assessment of nuclear waste repositories," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 561-573.
    4. Seyed Mohammad Asadzadeh & Hadi Maleki & Mehrab Tanhaeean, 2020. "A resilience engineering-based approach to improving service reliability in maintenance organizations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(5), pages 909-922, October.
    5. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
    6. Sujan, Mark A. & Habli, Ibrahim & Kelly, Tim P. & Gühnemann, Astrid & Pozzi, Simone & Johnson, Christopher W., 2017. "How can health care organisations make and justify decisions about risk reduction? Lessons from a cross-industry review and a health care stakeholder consensus development process," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 1-11.
    7. Gerassis, S. & Albuquerque, M.T.D. & García, J.F. & Boente, C. & Giráldez, E. & Taboada, J. & Martín, J.E., 2019. "Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 195-204.
    8. Seyed Mohammad Asadzadeh & Hadi Maleki & Mehrab Tanhaeean, 0. "A resilience engineering-based approach to improving service reliability in maintenance organizations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-14.
    9. Chiachío, Juan & Chiachío, Manuel & Prescott, Darren & Andrews, John, 2019. "A knowledge-based prognostics framework for railway track geometry degradation," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 127-141.
    10. P. Pablo Poveda-Orjuela & J. Carlos García-Díaz & Alexander Pulido-Rojano & Germán Cañón-Zabala, 2020. "Parameterization, Analysis, and Risk Management in a Comprehensive Management System with Emphasis on Energy and Performance (ISO 50001: 2018)," Energies, MDPI, vol. 13(21), pages 1-43, October.
    11. Aven, Terje & Renn, Ortwin, 2018. "Improving government policy on risk: Eight key principles," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 230-241.
    12. Zhang, Qian & Pan, Ning & Meloni, Marco & Lu, Dong & Cai, Jianguo & Feng, Jian, 2021. "Reliability analysis of radially retractable roofs with revolute joint clearances," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    13. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    14. Francis, Royce & Bekera, Behailu, 2014. "A metric and frameworks for resilience analysis of engineered and infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 90-103.
    15. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    16. repec:arp:tjssrr:2019:p:69-75 is not listed on IDEAS
    17. Fang, Jiayue & Kang, Rui & Chen, Ying, 2021. "Reliability evaluation of non-repairable systems with failure mechanism trigger effect," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    18. Mussard, Stéphane & Pi Alperin, María Noel, 2021. "Accounting for risk factors on health outcomes: The case of Luxembourg," European Journal of Operational Research, Elsevier, vol. 291(3), pages 1180-1197.
    19. Aven, Terje, 2013. "A conceptual framework for linking risk and the elements of the data–information–knowledge–wisdom (DIKW) hierarchy," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 30-36.
    20. Amro Nasr & Oskar Larsson Ivanov & Ivar Björnsson & Jonas Johansson & Dániel Honfi, 2021. "Towards a Conceptual Framework for Built Infrastructure Design in an Uncertain Climate: Challenges and Research Needs," Sustainability, MDPI, vol. 13(21), pages 1-19, October.
    21. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.

    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:eee:reensy:v:180:y:2018:i:c:p:136-151. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.