Condition-Based Maintenance with both Perfect and Imperfect Maintenance Actions
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper deals with a condition-based maintenance (CBM) model considering both perfect and imperfect maintenance actions for a deteriorating system whose condition is aperiod- ically monitored according to a remaining useful life (RUL) based-inspection policy. Perfect maintenance actions restore completely the system to the ’as good as new’ state. Their related cost are however often high. Imperfect preventive maintenance restores partially the system with reduced main- tenance cost. Nevertheless, it may however make the system more susceptible to future deterioration. The aim of the pa- per is to propose a CBM model which can help to construct optimal maintenance policies when both perfect and imper- fect maintenance actions are possible. To illustrate the use of the proposed CBM model, a numerical example finally is introduced.
How to Cite
##plugins.themes.bootstrap3.article.details##
condition based maintenance (CBM), remaining useful life (RUL), Degradation Model, imperfect maintenance
Castro, I. T. (2009). A model of imperfect preventive maintenance with dependent failure modes. European Journal of Operational Research, 196(1), 217 – 224.
Cui, L., Xie, M., & Loh, H.-T. (2004). Inspection schemes for general systems. IIE Transactions, 39(9), 817–825. Do Van, P., & Berenguer, C. (2010). Condition based maintenance model for a production deteriorating system. In Conference on control and fault-tolerant systems (sys-tol’10), 6-8 september 2010, nice, france. IEEE.
Do Van, P., & Berenguer, C. (2012). Condition-based maintenance with imperfect preventive repairs for a deteriorating production system. Reliability and Quality En-
gineering International, DOI: 10.1002/qre.1431.
Do Van, P., Vu, H. C., Barros, A., & Berenguer, C. (2012). Grouping maintenance strategy with availability constraint under limited repairmen. In 8th ifac international symposium on fault detection, supervision and safety for technical processes, safe process-2012, 29-31 august 2012, mexico city, mexico.
Gebraeel, N., Lawley, M., Li, R., & Ryan, J. (2005). Residual-life distributions from components degradation signals : A bayesian approach. IIE Transactions, 37, 543–557.
Ghasemi, A., Yahcout, S., & Ouali, M. (2007). Optimal condition based maintenance with imperfect information and the proportional hazards model. International Journal of Production Research, 4, 989–1012.
Liu, Y., & Huang, H. (2010). Optimal selective maintenance strategy for multi-state systems under imperfect maintenance. IEEE Transactions On Reliability, 59(2), 356 – 367.
Meier-Hirmer, C., Riboulet, G., Sourget, F., & Roussignol, M. (2008). Maintenance optimisation for a system with a gamma deterioration process and intervention delay: application to track maintenance. Journal of Risk and Reliability, 223, 189–198.
Mettas, A. (2000). Reliability allocation and optimization for complex systems. In Ieee proceedings of the annual reliability and maintainability symposium (pp. 216 – 221).
Nakagawa, T., & Yasui, K. (1987). optimal policies for a system with imperfect maintenance. IEEE Transactions On Reliability, R-36(5), 631–633.
Neves, M. L., Santiago, L., & Maia, C. (2011). A condition- based maintenance policy and input parameters estimation for deteriorating systems under periodic inspection. Computers and Industrial Engineering, 61, 503– 511.
Nicolai, R. P., Frenk, J., & Dekker, R. (2009). Modelling and optimizing imperfect maintenance of coatings on steel structures. Structural Safety, 31, 234 – 244.
Noortwijk, J. van. (2009). A survey of the application of Gamma processes in maintenance. Reliability Engineering and System Safety, 94, 2–21.
Pham, H., & Wang, H. (1996). Imperfect maintenance. European Journal of Operational Research, 94, 425–438.
Ponchet, A., Fouladirad, M., & Grall, A. (2011). Maintenance policy on a finite time span for a gradually deteriorating system with imperfect improvements. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 225(2), 105–116.
Ross, S. (1996). Stochastic processes. New york, John Wiley & Sons, Inc.
Tan, L., Cheng, Z., Guo, B., & Gong, S. (2010). Condition- based maintenance policy for gamma deteriorating systems. Journal of Systems Engineering and Electronics, 21, 57–61.
Wu, S., & Zuo, J. M. (2010). Linear and nonlinear preventive maintenance models. IEEE Transactions On Reliability, 59(1), 242 – 249.
Yang, Y., & Klutke, G.-A. (2001). A distribution-free lower bound for availability of quantile-based inspection schemes. IEEE Transactions On Reliability, 50, 419–421.
Kijima, M., Morimura, H., & Suzuki, Y. (1988). Periodical replacement problem without assuming minimal repair. European Journal of Operational Research, 37(2), 194–203.
Kurt, M., & Kharoufeh, J. (2010). Optimally maintaining a markovian deteriorating system with limited imperfect repairs. European Journal of Operational Research, 205, 368–380.
Labeau, P.-E., & Segovia, M.-C. (2010). Effective age models for imperfect maintenance. Journal of Risk and Reliability, 225, 117–130.
Levitin, G., & Lisnianski, A. (2000). Optimization of im- perfect preventive maintenance for multi-state systems. Reliability Engineering and System Safety, 67, 193– 203.
Lie, C. H., & Chun, Y. (1986). An algorithm for preventive maintenance policy. IEEE Transactions On Reliability, 35(1), 71 – 75.
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.