Maintenance Planning Optimization Based on PHM Information and Spare Parts Availability
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Maintenance planning plays an important role in assets management because it directly affects assets availability. In the aviation industry, maintenance planning becomes even more important due to the high availability expectations from aircraft operators and the high costs incurred when an aircraft becomes out of service. Gathering and combining all the relevant information to generate an optimized maintenance planning is not a simple task because the number of variables to be considered is high. The aim of this paper is to present a new model to plan maintenance interventions, using RUL (Remaining Useful Life) estimations obtained from a PHM (Prognostics and Health Monitoring) system. This information is used to verify whether spare parts will be available when the next failures are expected to occur. Since spare parts are finite resources, the goal of the proposed model is to reduce the probability that multiple similar components will fail in a short period of time because, when it happens, there is not enough time to repair all failed components and fleet availability is penalized. To avoid this situation, the model suggests the anticipation of some replacements. This paper presents a simulation comparing a situation in which PHM information is not available with the proposed model in terms of fleet availability and investment in spare parts. Life cycle cost considering a time horizon of 15 years was also computed in simulations. The results showed that the proposed model allowed an increase in fleet availability and a reduction in the lifecycle cost.
How to Cite
##plugins.themes.bootstrap3.article.details##
prognostics, inventory management, spare parts
Do Van, P., Voisin, A., Levrat, E. & Iung, B. (2012). Condition-Based Maintenance with both Perfect and Imperfect Maintenance Actions. In Proceedings of Annual Conference of the Prognostics and Health Management Society.
Doyen, L. & Gaudoin, O. (2004). Classes of Imperfect Repair Models Based on Reduction of Failure Intensity or Virtual Age. Reliability Engineering and System Safety, Volume 84.
Fritzsche, R. & Lasch, R. (2012). An Integrated Logistics Model of Spare Parts Maintenance Planning within the Aviation Industry. World Academy of Science, Engineering and Technology, Volume 68.
Leão, B. P., Yoneyama, T., Rocha, G. C. & Fitzgibbon, K. T. (2008). Prognostics Performance Metrics and their Relation to Requirements, Design, V erification and Cost-Benefit. In Proceedings of International Conference on Prognostics and Health Management, Denver.
Lee, L. H., Chew, E. P., Teng, S. & Chen, Y. (2008). Multi- Objective Simulation-Based Evolutionary Algorithm for an Aircraft Spare Parts Allocation Problem. European Journal of Operational Research, Volume 189.
Perlman, Y. & Levner, I. (2010). Modeling Multi-Echelon Multi-Supplier Repairable Inventory Systems with Backorders. Journal of Service Science and Management, Volume 3.
Rodrigues, L. R. & Yoneyama, T. (2012). Spare Parts Inventory Control for Non-Repairable Items Based on Prognostics and Health Monitoring Information. In Proceedings of Annual Conference of the Prognostics and Health Management Society.
Sandborn, P. A. & Wilkinson, C. (2007). A Maintenance Planning and Business Case Development Model for the Application of Prognostics and Health Management (PHM) to Electronic Systems. Microelectronics Reliability, Volume 47, Issue 12, Electronic system prognostics and health management.
Sherbrooke, C. C. (2004). Optimal Inventory Modeling of Systems: Multi-Echelon Techniques. In 2nd. ed. Springer.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. In 1st ed. Hoboken.
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.