IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v8y2017i2d10.1007_s13198-016-0496-1.html
   My bibliography  Save this article

Performance modeling of the serial processes in refining system of a sugar plant using RAMD analysis

Author

Listed:
  • Anil Kr. Aggarwal

    (YMCAUST)

  • Sanjeev Kumar

    (YMCAUST)

  • Vikram Singh

    (YMCAUST)

Abstract

The main objective of this paper is to present a method to identify the critical component of the system. As traditionally, any one parameter among availability, reliability and maintainability parameters is computed to identify the critical component and its effect on performance of the system. In this paper, reliability, availability, maintainability and dependability (RAMD) parameters or indices are computed to identify the critical component of the system. Mathematical modeling of the system based on Markov birth–death process is carried out to derive Chapman–Kolmogorov differential equations. These equations are further solved and RAMD parameters are computed with mean time between failures (MTBF), mean time to repair (MTTR) and dependability ratio parameters for each component of the system. Sensitivity analysis has been conducted for finding the most critical component of the system by varying the failure and repair rates of each subsystem of the system. To show the application of the proposed method, a case of the refining system, a repairable industrial system of sugar plant has been taken for evaluating RAMD indices of the system.

Suggested Citation

  • Anil Kr. Aggarwal & Sanjeev Kumar & Vikram Singh, 2017. "Performance modeling of the serial processes in refining system of a sugar plant using RAMD analysis," 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. 8(2), pages 1910-1922, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0496-1
    DOI: 10.1007/s13198-016-0496-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-016-0496-1
    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/s13198-016-0496-1?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. Sharma, Rajiv Kumar & Kumar, Sunand, 2008. "Performance modeling in critical engineering systems using RAM analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 913-919.
    2. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.
    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. Rezgar Zaki & Abbas Barabadi & Ali Nouri Qarahasanlou & A. H. S. Garmabaki, 2019. "A mixture frailty model for maintainability analysis of mechanical components: a case study," 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. 10(6), pages 1646-1653, December.

    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. Yashi Vishwakarma & S. P. Sharma, 2016. "Uncertainty analysis of an industrial system using Intuitionistic Fuzzy Set Theory," 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. 7(1), pages 73-83, March.
    2. Fan, Mengfei & Zeng, Zhiguo & Zio, Enrico & Kang, Rui & Chen, Ying, 2018. "A stochastic hybrid systems model of common-cause failures of degrading components," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 159-170.
    3. Anil Kr. Aggarwal & Vikram Singh & Sanjeev Kumar, 2017. "Availability analysis and performance optimization of a butter oil production system: a case study," 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. 8(1), pages 538-554, January.
    4. Hamzeh Soltanali & A.H.S Garmabaki & Adithya Thaduri & Aditya Parida & Uday Kumar & Abbas Rohani, 2019. "Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing," Journal of Risk and Reliability, , vol. 233(4), pages 682-697, August.
    5. Giacomo Barbieri & Jose Daniel Hernandez, 2024. "Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability," Sustainability, MDPI, vol. 16(3), pages 1-23, January.
    6. Anil Kr. Aggarwal & Sanjeev Kumar & Vikram Singh, 2017. "Mathematical modeling and reliability analysis of the serial processes in feeding system of a sugar plant," 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. 8(1), pages 435-450, January.
    7. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(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:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0496-1. 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.