IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-319-62319-1_3.html
   My bibliography  Save this book chapter

Mathematical Models for Reliability Allocation and Optimization for Complex Systems

In: Human Factors and Reliability Engineering for Safety and Security in Critical Infrastructures

Author

Listed:
  • Domenico Falcone

    (University of Cassino and Southern Lazio)

  • Alessandro Silvestri

    (University of Cassino and Southern Lazio)

  • Gianpaolo Bona

    (University of Cassino and Southern Lazio)

  • Antonio Forcina

    (University of Cassino and Southern Lazio)

Abstract

RAMS is an acronym for Reliability, Availability, Maintainability and Safety. These four properties concern the application of important methodologies for designing and managing complex technical systems. The present chapter analyses several reliability allocation techniques present in literature. Starting from well-known methodologies, two reliability allocation methods has been proposed and validated: Integrated Factors Method (I.F.M.) and Critical Flow Method (C.F.M.). We focus on the most important conventional methods to discuss their limitations to motivate the current research. The proposed methods supply a logic for the analysis of prototype complex systems during the pre-design phase, even if it presents general characteristics that allow this logic to be extended to different design phases. In particular, the proposed CFM method can resolve the shortcomings of the conventional methods with a new reliability approach useful to series-parallel configurations in order to obtain important cost savings. In fact, the results show that the most conventional reliability allocation methods have one fundamental problem: in general, they are designed for complex system with series-configurations (preliminary phase design) but not for series-parallel configurations. The result is an increase of reliability allocated to units (series configuration) in order to guarantee the reliability target system (extremely low failure rate).

Suggested Citation

  • Domenico Falcone & Alessandro Silvestri & Gianpaolo Bona & Antonio Forcina, 2018. "Mathematical Models for Reliability Allocation and Optimization for Complex Systems," Springer Series in Reliability Engineering, in: Fabio De Felice & Antonella Petrillo (ed.), Human Factors and Reliability Engineering for Safety and Security in Critical Infrastructures, pages 43-76, Springer.
  • Handle: RePEc:spr:ssrchp:978-3-319-62319-1_3
    DOI: 10.1007/978-3-319-62319-1_3
    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.

    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:ssrchp:978-3-319-62319-1_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.

    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.