Cost Comparison of Maintenance Policies
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Maintenance is crucial to all repairable engineering systems as they will degrade and fail. The cost of maintenance for a manufacturing plant can occupy up to 30% of the total operating cost. If maintenance is not scheduled properly, unexpected equipment failure can induce significant cost due to reduced productivity and sub-standard products produced, both of which may result in customer penalty. Various maintenance policies have been proposed in the past. Among the various policies, age-dependent and periodic maintenances are the common policies employed in industries. Recently, predictive maintenance or condition based maintenance policies are also proposed owing to the advancement in the sensor technology. In this work, we compare the age-dependent and periodic maintenance policies as well as the predictive maintenance policies from the perspective of cost using Markov multi-state maintenance modeling and Monte Carlo simulation. To be realistic, imperfect maintenance is included, and both the sequential and continuous inspections are considered and compared.
How to Cite
##plugins.themes.bootstrap3.article.details##
age-dependent maintenance, periodic maintenance, condition-based maintenance, sequential inspection, continuous inspection, imperfect maintenance, Monte Carlo simulation
Berg, M., & Epstein, B. (1976). A modified block replacement policy. Naval Research Logistics Quarterly, 23(1), 15-24. Brown, M., & Proschan, F. (1983). Imperfect repair. Journal of
Applied Probability, 20(4), 851-859.
Chan, J.K., & Shaw, L. (1993). Modeling repairable systems with failure rates that depend on age and maintenance. Reliability, IEEE Transactions on, 42(4), 566-571.
Drenick, RF. (1960). The failure law of complex equipment.Journal of the Society for Industrial and Applied Mathematics, 8(4), 680-690.
Grall, A., Dieulle, L., Bérenguer, C., & Roussignol, M. (2002). Continuous-time predictive-maintenance scheduling for a deteriorating system. Reliability, IEEE Transactions on, 51(2), 141-150.
Kijima, M. (1989). Some results for repairable systems with general repair. Journal of Applied Probability, 26(1), 89-102.
Kijima, M., Morimura, H., & Suzuki, Y. (1988). Periodical replacement problem without assuming minimal repair. European Journal of Operational Research, 37(2), 194-203.
Lam, CT, & Yeh, RH. (1994). Optimal maintenance-policies for deteriorating systems under various maintenance strategies. Reliability, IEEE Transactions on, 43(3), 423-430.
Levitin, G., & Lisnianski, A. (2000). Optimization of imperfect preventive maintenance for multi-state systems. Reliability Engineering & System Safety, 67(2), 193-203.
Lu, S., Tu, Y.C., & Lu, H. (2007). Predictive condition based maintenance for continuously deteriorating systems. Quality and Reliability Engineering International, 23(1), 71-81.
Malik, MAK. (1979). Reliable preventive maintenance policy. AIIE transactions, 11(3), 221-228.
Martorell, S., Sanchez, A., & Serradell, V. (1999). Age-dependent reliability model considering effects of maintenance and working conditions. Reliability Engineering & System Safety, 64(1), 19-31.
Ming Tan, C., & Raghavan, N. (2008). A framework to practical predictive maintenance modeling for multi-state systems. Reliability Engineering & System Safety, 93(8), 1138-1150.
Moustafa, MS, Maksoud, EY, & Sadek, S. (2004). Optimal major and minimal maintenance policies for deteriorating systems. Reliability Engineering & System Safety, 83(3), 363-368.
Murthy, DNP, Atrens, A., & Eccleston, JA. (2002). Strategic maintenance management. Journal of Quality in Maintenance Engineering, 8(4), 287-305.
Nakagawa, T. (1984). Optimal policy of continuous and discrete replacement with minimal repair at failure. Naval Research Logistics Quarterly, 31(4), 543-550.
Nakagawa, T. (1986). Periodic and sequential preventive maintenance policies. Journal of Applied Probability, 23(2), 536-542.
Nakagawa, T., & Yasui, K. (1987). Optimum policies for a system with imperfect maintenance. Reliability, IEEE Transactions on, 36(5), 631-633.
Ohnishi, M., Kawai, H., & Mine, H. (1986). An optimal inspection and replacement policy for a deteriorating system. Journal of Applied Probability, 23(4), 973-988.
Pham, H., & Wang, H. (1996). Imperfect maintenance. European Journal of Operational Research, 94(3), 425-438.
SHEU, S.H., KUO, C.M., & NAGAGAWA, T. (1993). Extended optimal age replacement policy with minimal repair. RAIRO. Recherche opérationnelle, 27(3), 337-351.
Tomasevicz, C.L., & Asgarpoor, S. (2009). Optimum maintenance policy using semi-Markov decision processes. Electric Power Systems Research, 79(9), 1286-1291.
Wang, H., & Pham, H. (1996). A quasi renewal process and its applications in imperfect maintenance. International journal of systems science, 27(10), 1055-1062.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.