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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 94
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Paper 88

Particle Swarm Optimization: Application in Maintenance Optimization

S. Carlos1, A. Sanchez2, S. Martorell1 and J.-F. Villanueva1

1Department of Chemical and Nuclear Engineering,
2Department of Applied Statistics and Operational Research, and Quality,
Universidad Politécnica de Valencia, Spain

Full Bibliographic Reference for this paper
S. Carlos, A. Sanchez, S. Martorell, J.-F. Villanueva, "Particle Swarm Optimization: Application in Maintenance Optimization", in , (Editors), "Proceedings of the Seventh International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 88, 2010. doi:10.4203/ccp.94.88
Keywords: reliability, availability, cost, maintenance, surveillance test, optimization, particle swarm optimization.

Many industrial sectors are concerned with developing optimal maintenance planning because of the importance of maintenance on the economy and safety. Traditionally, maintenance planning is formulated in terms of a multi-objective optimization problem where reliability, availability, maintainability and cost act as decision criteria; and surveillance tests and maintenance strategies act as decision variables. However, the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources available to implement such strategies.

These problems have been successfully solved using meta-heuristics optimization techniques, for example, evolutionary algorithms, tabu search, ant colony optimization, etc. [1,2,3,4]. There exist different strategies to obtain a set of solutions of a multiobjective optimization problem [5]. One of those approaches consists of combining all the criteria involved in the optimization problem into one objective function. That is, transforming the multi objective into a single objective optimization problem.

Particle swarm optimization (PSO) is a stochastic global optimization technique inspired by social behaviour of bird flocking or fish schooling. It simulates the feature of bird flocking and fish schooling to configure the heuristic learning mechanism. The learning procedure of PSO is based on modifying the solution of each individual particle with the aim of its own best experience and other individuals' best experiences [6].

In this paper, the maintenance plan optimization of a high pressure injection system (HPIS) of a nuclear power plant is performed. HPIS can be used to remove heat from the reactor under accidental conditions. It is of great importance to maintain the plant in safe operating conditions and this requires an adequate maintenance plan to guarantee a high level of availability, which results in an increase of the resources required. Using PSO it has been possible to obtain a set of feasible solutions to be implemented in the plant.

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M. Harunuzzaman, T. Aldemir, "Optimization of standby safety system maintenance schedules in nuclear power plants", Nuclear Technology, 113(3), 354-367, 1996.
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C. Lapa, C. Pereira, P. Melo, "Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints", Reliability Engineering and System Safety, 81(1), 103-109, 2003. doi:10.1016/S0951-8320(03)00085-1
S. Martorell, A. Sánchez, S. Carlos, V. Serradell, "Alternatives and challenges in optimizing industrial safety using genetic algorithms", Reliability Engineering and System Safety, 86, 25-38, 2003. doi:10.1016/j.ress.2003.12.010
J. Kennedy, R.C. Eberhart, "Swarm Intelligence", Morgan Kauffmann Publishers, 2001.

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