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

Author

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  • 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
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    References listed on IDEAS

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    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, March.
    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.
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