Availability analysis of a cooking oil production line
Subject Areas : Business AdministrationAfshin Yaghoubi 1 , Saeed Rahimi 2 , Roya Soltani 3 , Seyed Taghi Akhavan Niaki 4
1 - Department of Industrial Engineering, Sharif University of Technology
2 - Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
3 - Department of Industrial Engineering, Khatam University, Tehran, Iran
4 - Department of Industrial Engineering, Sharif University of Technology
Keywords: reliability, Availability, Markov process, Corrective maintenance, Pareto diagram,
Abstract :
Availability and reliability of a manufacturing system are the most common indicators in the reliability engineering area to assess the quality and on-time deliveries of the products they produce. The purpose of this paper is to analyze the availability, reliability. failure metrics such as MTBF and MTTF, and also steady-state availability of a cooking oilproduction line using a Markov approach. The product line works in three consecutive shifts 24 hours a day, for which five main subsystems are identified for the analysis. The results show that the first shift has the best performance in terms of reliability while the second shift has the worst performance. To improve the reliability of the production line, a corrective maintenance policy is used. First, the critical components of the subsystems are identified using the Pareto charts, and then, by increasing the repair rates, the availability of the production line in all three shifts is increased.
Amiri, M., Přenosil, V. (2014). General solutions for MTTF and steady-state availability of NMR systems. Reconfigurable and Communication-Centric Systems-on-Chip (ReCoSoC) 2014, 9th International Symposium on. IEEE, Montpellier, France.
Blischke, W. R., Prabhakar Murthy, D. N. (2003). Eds. Case studies in reliability and maintenance. Vol. 480. John Wiley & Sons.
Billinton, R., Allan, R. N. (1992). Reliability evaluation of engineering systems. New York: Plenum press.
Fazlollahtabar, H., & Saidi-Mehrabad, M. (2016). Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System. Journal of Optimization in Industrial Engineering, 9(19), 75-86.
Grosh, D. L. (1989). A primer of reliability theory. John Wiley & Sons.
Gupta, S., Tewari, P. C. (2011). Performance modeling of power generation system of a thermal plant. IJE Transactions A: Basics 24: 239-248.
Gupta, G., Mishra, R. P., Jain, P. (2015). Reliability analysis and identification of critical components using Markov model. Industrial Engineering and Engineering Management (IEEM), 2015 IEEE International Conference on. IEEE. Singapore, Singapore.
Hosseini, S. R., Amidpour, M., Behbahaninia, A. (2011). Thermoeconomic analysis with reliability consideration of a combined power and multi stage flash desalination plant. Desalination 278.1-3: 424-433.
Khalilnejad, A., Malek Pour, M., Zarafshan, E., Sarwat, A. (2016). Long term reliability analysis of components of photovoltaic system based on Markov process. SoutheastCon 2016. IEEE, Norfolk, VA, USA.
Kumar, A., Ram, M. (2013). Reliability measures improvement and sensitivity analysis of a coal handling unit for thermal power plant. International Journal of Engineering-Transactions C: Aspects 26.9: 1059-1066.
Rausand, M., Høyland, A. (2004). System reliability theory: models, statistical methods, and applications. Vol. 396. John Wiley & Sons.
Sharifi, M., & Yaghoubizadeh, M. (2015). Reliability modelling of the redundancy allocation problem in the series-parallel systems and determining the system optimal parameters. Journal of Optimization in Industrial Engineering, 8(17), 67-77.
Smith, D. T. (2014). Calculating the system steady-state availability as a function of subsystem steady-state availability. Southeastcon 2014, IEEE. Lexington, KY, USA.
Tan, Y., Feng, D. (2016). Reliability evaluation for unmanned aerial vehicle components based on Markov degradation process. Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016 IEEE. Xi'an, China.
Wang, J., Li, M., Ren, F., Li, X., & Liu, B. (2018). Modified exergoeconomic analysis method based on energy level with reliability consideration: Cost allocations in a biomass trigeneration system. Renewable Energy, 123, 104-116.
Wang, L., Yang, Q., & Tian, Y. (2017). Reliability analysis of 6-component star Markov repairable system with spatial dependence. Mathematical Problems in Engineering, 2017.
Wang, J., Zhang, Q., Yoon, S., & Yu, Y. (2019). Reliability and availability analysis of a hybrid cooling system with water-side economizer in data center. Building and Environment, 148, 405-416.
Yaghoubi, A., Niaki, S. T. A., Rostamzadeh (2020), H., "A closed-form equation for steady-state availability of cold standby repairable k-out-of-n", International Journal of Quality & Reliability Management, vol. 37, No. 1, 145-155.
Yang, D. Y., & Tsao, C. L. (2019). Reliability and availability analysis of standby systems with working vacations and retrial of failed components. Reliability Engineering & System Safety, 182, 46-55.
Zaidi, Z., Goyal, Y. K. (2014). Mathematical analysis and availability of the pulping system in the paper industry. International Journal of Modeling and Optimization 4.1: 31-37.
Zare, V. (2016). Exergoeconomic analysis with reliability and availability considerations of a nuclear energy-based combined cycle power plant. Energy, 96, 187-196.
Zhou, Y., Lin, C., Liu, Y., Xu, H. (2018). Analytical study on the reliability of redundancy architecture for flight control computer based on homogeneous Markov process." IEEE Access 6: 8290 - 18298.