IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-030-89647-8_14.html
   My bibliography  Save this book chapter

Towards Prognostics and Health Management of Multi-Component Systems with Stochastic Dependence

In: Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis

Author

Listed:
  • Roy Assaf

    (Autonomous Systems and Robotics Centre, University of Salford)

  • Phuc Do

    (University of Lorraine)

  • Phil Scarf

    (Cardiff University)

Abstract

Prognostics and health management can be described as an emerging engineering discipline which studies and associates the degradation processes to system lifecycle management. It allows for system health state assessment in real-time, as well as predicting its future health states. In this chapter we present a methodology that leads towards PHM of multi-component systems. We cover how to extract health indicators from multi-component systems and present a methodology which makes use of these indicators within a prognostics approach that allows considering stochastic dependence between components. We apply our methodology to data generated by a gearbox accelerated life testing platform. We show that compared to a reduced model for prognostics, where the stochastic dependence between components is not considered, our methodology predicts more accurately the components’ time of end of life.

Suggested Citation

  • Roy Assaf & Phuc Do & Phil Scarf, 2022. "Towards Prognostics and Health Management of Multi-Component Systems with Stochastic Dependence," International Series in Operations Research & Management Science, in: Adiel Teixeira de Almeida & Love Ekenberg & Philip Scarf & Enrico Zio & Ming J. Zuo (ed.), Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis, pages 305-320, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-89647-8_14
    DOI: 10.1007/978-3-030-89647-8_14
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

    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:isochp:978-3-030-89647-8_14. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.