IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10073-d888176.html
   My bibliography  Save this article

Development of Optimized Maintenance Program for a Steam Boiler System Using Reliability-Centered Maintenance Approach

Author

Listed:
  • Suyog S. Patil

    (Department of Mechanical Engineering, Zeal College of Engineering, Savitribai Phule Pune University, Pune 411041, Maharashtra, India
    Department of Mechanical Engineering, Sharad Institute of Technology College of Engineering, Ichalkaranji 416115, Maharashtra, India)

  • Anand K. Bewoor

    (Department of Mechanical Engineering, Cummins College of Engineering for Women, Savitribai Phule Pune University, Pune 411052, Maharashtra, India)

  • Ravinder Kumar

    (School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India)

  • Mohammad Hossein Ahmadi

    (Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran)

  • Mohsen Sharifpur

    (Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria 0002, South Africa
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan)

  • Seepana PraveenKumar

    (Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris, 19 Mira Street, 620002 Ekaterinburg, Russia)

Abstract

Reliability centered maintenance (RCM) is a new strategic framework for evaluating system maintenance requirements in its operating conditions. Some industries employ predictive maintenance strategies in addition to preventive maintenance (PM) strategies, which increase production costs. As the breakdown maintenance (BDM) technique is used, the maintenance cost increases. The RCM approach is a mixture of these maintenance strategies that can be used to optimize the maintenance costs and to ensure the availability of the system. The RCM method was applied to the steam boiler system used in the textile industries for the research work reported in this paper. The RCM methodology stated in the literature cannot be implemented, as it is in Indian textile industries due to the lack of knowledge of RCM principles, a labor-oriented nature, the use of partially computerized information systems, an inadequate maintenance database, and information about maintenance costs and production loss. To resolve these issues, a modified RCM approach involving a large number of experts is developed. To apply this RCM methodology, critical components are identified through reliability and failure mode effect and criticality analysis (FMECA). Finally, scheduled maintenance strategies and their intervals are recommended to ensure that the system continues to operate properly. According to this study, implementing the RCM technique effectively will increase boiler system reliability and availability by 28.15 percent and 0.16 percent, respectively. Additionally, up to 20.32 percent of the maintenance cost can be saved annually by applying these scheduled maintenance programs.

Suggested Citation

  • Suyog S. Patil & Anand K. Bewoor & Ravinder Kumar & Mohammad Hossein Ahmadi & Mohsen Sharifpur & Seepana PraveenKumar, 2022. "Development of Optimized Maintenance Program for a Steam Boiler System Using Reliability-Centered Maintenance Approach," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10073-:d:888176
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10073/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10073/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Selvik, J.T. & Aven, T., 2011. "A framework for reliability and risk centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 324-331.
    2. Hongzhou Wang & Hoang Pham, 2006. "Reliability and Optimal Maintenance," Springer Series in Reliability Engineering, Springer, number 978-1-84628-325-3, December.
    3. Macchi, Marco & Garetti, Marco & Centrone, Domenico & Fumagalli, Luca & Piero Pavirani, Gian, 2012. "Maintenance management of railway infrastructures based on reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 71-83.
    4. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Das, K. & Lashkari, R.S. & Sengupta, S., 2007. "Machine reliability and preventive maintenance planning for cellular manufacturing systems," European Journal of Operational Research, Elsevier, vol. 183(1), pages 162-180, November.
    6. David F. Percy, 2008. "Preventive Maintenance Models for Complex Systems," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 8, pages 179-207, Springer.
    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. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.

    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. Alebrant Mendes, Angélica & Duarte Ribeiro, José Luis, 2014. "Establishment of a maintenance plan based on quantitative analysis in the context of RCM in a JIT production scenario," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 21-29.
    2. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    3. Hashemi, M. & Asadi, M. & Zarezadeh, S., 2020. "Optimal maintenance policies for coherent systems with multi-type components," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. Yuqian Xu & Lingjiong Zhu & Michael Pinedo, 2020. "Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls," Operations Research, INFORMS, vol. 68(6), pages 1804-1825, November.
    5. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    7. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Vikrant Sharma & Sundeep Kumar & M. L. Meena, 2022. "Key criteria influencing cellular manufacturing system: a fuzzy AHP model," Journal of Business Economics, Springer, vol. 92(1), pages 65-84, January.
    9. Nitin Panwar & Sanjeev Kumar, 2022. "Mathematical modelling and performance analysis of screening unit in paper 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. 13(5), pages 2751-2763, October.
    10. Ben Mabrouk, A. & Chelbi, A. & Radhoui, M., 2016. "Optimal imperfect maintenance strategy for leased equipment," International Journal of Production Economics, Elsevier, vol. 178(C), pages 57-64.
    11. Ji Hwan Cha & Maxim Finkelstein, 2020. "On optimal life extension for degrading systems," Journal of Risk and Reliability, , vol. 234(3), pages 487-495, June.
    12. Maxim Finkelstein & Ji Hwan Cha, 2022. "Reducing degradation and age of items in imperfect repair modeling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1058-1081, December.
    13. Dharmaraja, S. & Vinayak, Resham & Trivedi, Kishor S., 2016. "Reliability and survivability of vehicular ad hoc networks: An analytical approach," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 28-38.
    14. Lu, Biao & Zhou, Xiaojun, 2017. "Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 116-127.
    15. Jakov Batelić & Karlo Griparić & Dario Matika, 2021. "Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets," Energies, MDPI, vol. 14(18), pages 1-14, September.
    16. Chengye Ma & Yongjun Du & Lijun Shang & Li Yang & Kaiye Gao, 2023. "Random Maintenance Strategy Modeling of Warranted Products with Reliability Heterogeneity," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    17. Marlow, David R. & Beale, David J. & Mashford, John S., 2012. "Risk-based prioritization and its application to inspection of valves in the water sector," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 67-74.
    18. Bouslah, B. & Gharbi, A. & Pellerin, R., 2016. "Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint," Omega, Elsevier, vol. 61(C), pages 110-126.
    19. Rokhforoz, Pegah & Montazeri, Mina & Fink, Olga, 2023. "Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    20. Panagiotis H. Tsarouhas & George K. Fourlas, 2016. "Mission reliability estimation of mobile robot system," 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(2), pages 220-228, June.

    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:gam:jsusta:v:14:y:2022:i:16:p:10073-:d:888176. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.