International Journal of Reliability, Risk and Safety: Theory and Application

International Journal of Reliability, Risk and Safety: Theory and Application

Reliability Prediction of a Mechanical Refiner

Document Type : Original Research Article

Author
Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran
Abstract
Reliability refers to the quality during operation, the fulfillment of requirements, and the quality production of the final product by a system. A Refiner is also industrial equipment that is used to improve the properties of the raw material and prepare it for the production of the final product. In the mechanical pulp production industry, the role of the refiner in product production and its effect on product quality and total cost is very important. In this article, the structure of a mechanical refiner is introduced, and the main components effective in determining and improving reliability are investigated, and their reliability values are calculated.  The most influential element of this equipment, based on the calculations, is the mechanical seal, which should be more careful than other elements in selecting and monitoring its condition to achieve the reliability of the target.
Keywords
Subjects

  1. A. Farsi, Principles of Reliability Engineering, 2nd ed. Tehran: Symaye Danesh, 2023, Second edition.
  2. Østeras, D.N.P. Murthy, and M. Rausand, “Reliability performance and specifications in new product development,” Norwegian University of Science and Technology, Trondheim, Norway, 2022.
  3. RausandA. BarrosArnljot Hoyland, “System Reliability Theory: Models, Statistical Methods, and Applications”, Wiley, 2020, doi: https://doi.org/10.1002/9781119373940.fmatter.
  4. Paulapuro, Papermaking Part 1, Stock Preparation and Wet End, Georgia, USA: TAPPI Press, 2000.
  5. Jibril, V.V. Singh, and D. K Rawal, “Probabilistic assessment of complex system consisting three subsystems multi failure threats and copula repair approach,” International Journal of Quality Reliability and Management, vol. 40, no. 1, pp. 83-102, 2021, doi: https://doi.org/10.1108/IJQRM-03-2021-0061.
  6. Raghav, D. K. Rawal, I. Yusuf, R. H. Konkoffi, and V. V. Singh, “Reliability prediction of distributed system with homogeneity in software and server subject to different repair policies using joint probability distribution via copula approach,” Reliability: Theory and Applications, vol.16, no. 1, pp. 217-230, 2021, doi: https://doi.org/10.24412/1932-2321-2021-161-217-230.
  7. o Shi , J. Zhang, E. Zio, and X. Zhao, “Opportunistic maintenance policies for multi-machine production systems with quality and availability improvement,” Reliability Engineering and System Safety, Vol. 234, 2023, Art. no. 109183, doi: https://doi.org/10.1016/j.ress.2023.109183
  8. Qi , M. Huang, “Joint optimization of maintenance and spares inventory policy for a series-parallel system considering dependent failure processes,” Reliability Engineering and System Safety, vol. 247, 2024, Art. no. 110116 doi: https://doi.org/10.1016/j.ress.2024.110116.
  9. Sachdeva, D. Kumar, P. Kumar, “Reliability analysis of pulping system using Petri nets,” International Journal of Quality and Reliability Management, vol. 25, no. 8, pp. 860-877, 2008, doi: https://doi.org/10.1108/02656710810898667.
  10. Kumar, I.p. Singh, and J. Singh, “Reliability analysis of the feeding system in the paper industry”, Microelectronics Reliability, vol. 28, no. 2, pp.213-215, 1988, doi: https://doi.org/10.1016/0026-2714(88)90353-8.
  11. Kumar, J. Singh, and P.C. Pandey, “Availability of a washing system in the paper industry,”  Microelectronics Reliability, vol. 29, no. 5, pp. 775-778, 1989, doi: https://doi.org/10.1016/0026-2714(89)90177-7.
  12. F. Zaidi and Y.K. Goyal, “Mathematical analysis and availability of the pulping system in the paper industry”, International Journal of Modeling and Optimization, vol. 4, no. 1, pp. 31-37, 2014, doi: https://doi.org/10.7763/IJMO.2014.V4.343.
  13. Kumar, M. Goel, and N. Ling, “Fuzzy reliability analysis of a pulping system in paper industry with general distributions for all random variables,” Cogent Mathematics, vol. 4, no. 1, 2017, Art. no. 1285467, doi: https://doi.org/10.1080/23311835.2017.1285467.
  14. Saini, J. Kumar, and M.S. Kadyan, “Performance analysis of sinter system of steel plant using supplementary variable technique”, International Journal of System Assurance Engineering and Management, Vol. 15, pp. 2931-2949, 2024, doi: https://doi.org/10.1007/s13198-024-02305-y.
  15. Gurunathan, "Availability modeling and estimation of Fluid Catalytic Cracking Unit using generalized Stochastic Petri Nets", International Journal of Quality and Reliability Management, vol. 38, no. 7, pp. 1628-1659, 2021, doi: https://doi.org/10.1108/IJQRM-07-2019-0242.  
  16. Bajpai, Green Chemistry and Sustainability in Pulp and Paper Industry, Springer: 2015, doi: http://dx.doi.org/10.1007/978-3-319-18744-0.
  17. Bordin, J.C. Roux, and J.F. Bloch, “Global description of refiner plate wear in low consistency beating”, Nordic Pulp and Paper Research Journal ,Vol 22, no. 4, pp. 529-534, 2007, doi: https://doi.org/10.3183/npprj-2007-22-04-p529-534.
  18. Frazier, D.R. Danks, and B. Hodge, “Paper pulp refiner long-duration wear monitoring with polymer replicas”, Wear, vol. 267, no. 5-8, pp. 1095–1099, 2009, doi: https://doi.org/10.1016/j.wear.2009.01.017.
  19. w. Lourari, A. Soualhi, K. Medjaher, and T. Benkedjouh, “New health indicators for the monitoring of bearing failures under variable loads”, Structural Health Monitoring, vol.23 , no. 5, 2024, doi: https://doi.org/10.1177/14759217231219486.
  20. A. Farsi, “Identification of size and location of bearing damage via deep learning”. International Journal of Reliability, Risk and Safety: Theory and Application, vol.4, no.1, pp. 69-74, 2021, doi: https://doi.org/10.30699/IJRRS.4.1.9.
  21. American Roller Bearing Company, “Bearing selection, load and life.” [Online]. Available: amroll.com/bearing-selection-load-life.html.
  22. Handbook of Reliability Prediction Procedures for Mechanical equipment, Maryland, USA: Naval Surface Walfare Center, 2010.
  23. N. Li, G. Tong, J. Yang, G. Sun, D. Han, and G. Wang, “Reliability prediction approaches for domestic intelligent electric energy meter based on IEC62380”, IOP Conference Series: Earth and Environmental Science, vol. 108, no. 5, 2018, Art. no.  052030, doi: https://iopscience.iop.org/article/10.1088/1755-1315/108/5/052030
Volume 7, Issue 2
October 2024
Pages 71-78

  • Receive Date 05 November 2024
  • Revise Date 07 December 2024
  • Accept Date 08 December 2024