Application of Genetic Algorithms to Estimate Preventive Maintenance Interval: A Pump Device as a Case Study
Keywords:Preventive maintenance, Centrifugal Pumps Maintenance, Application of genetic algorithm
Genetic Algorithms is one of the techniques which has been applied in recent years to contribute to the scheduling of Preventive Maintenance (PM) to improve the whole system performance and run efficiently and effectively. This paper aims to estimate the interval of preventive maintenance for any processing equipment using genetic algorithms. A centrifugal pump is presented as critical equipment to apply a genetic algorithm to prolong the interval of PM. The obtained results show that a genetic algorithm is effective and practical in estimating the optimum time of preventive maintenance for centrifugal pumps.
Kobbacy, K., 2012. Application of artificial intelligence in maintenance modelling and management, 2nd IFAC workshop on advanced maintenance engineering, services and technology Universidad de Sevilla, Sevilla, Spain, 2012, November 22-23.
Elwerfalli, A., Khan, M. K., and Munive, J., 2018. Developing Turnaround Maintenance (TAM) Model to Optimise TAM scheduling for Gas Plants Based on Critical Static Equipment, International Journal of Industrial Engineering and Operations Management (IJIEOM).
Haupt, R., and Haupt, S., 2004. Practical Genetic Algorithms, 2nd Edition. John Wiley & Sons, Inc., Hoboken, New Jersey.
Lapa, C., Cláudio, M., Pereira, C., and Paes de Barros, M., 2006. A model for preventive maintenance planning by genetic algorithms based in cost and reliability, Reliability Engineering &System Safety, 91(2), pp. 233-240.
Volkanovski, A.,Mavko, B., Boševski, T., Čauševski, A., and Čepin, M., 2008. Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system, Reliability Engineering & System Safety, 93(6), pp.779-789.
Tsai, Y., Wang, K., and Teng, H., 2001. Optimizing preventive maintenance for mechanical components using genetic algorithms. Reliability Engineering and System Safety, 74(1), pp. 89-97.
Sortrakul, N., Nachtmann, H., and Cassady, C., 2005. Genetic algorithms for integrated preventive maintenance planning and production scheduling for a single machine. Computers in Industry, 56 (2), pp. 161-168.
Yang, Z., and Yang, G., 2012. Optimization of Aircraft Maintenance plan based on Genetic Algorithm. Physics Procedia, 33, pp. 580-586. 165.
Levitin, G., and Lisnianski, A., 2000. Optimization of imperfect preventive maintenance for multi-state systems. Reliability Engineering and System Safety, 67(2), pp.193-203.
Monga, A., and Zuo, M., 2001. Optimal design of series-parallel systems considering maintenance and salvage values. Computers and Industrial Engineering, 40(4), pp. 323-337.
Nahas, N., Khatab, A., Ait-Kadi, D., and Nourelfath, M., 2008. Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems. Reliability Engineering and System Safety, 93(11), pp.1658-1672.
Nourelfath, M., Châtelet, E., and Nahas, N., 2012. Joint redundancy and imperfect preventive maintenance optimization for series-parallel multi-state degraded systems. Reliability Engineering and System Safety, 103, pp.51-60.
Marseguerra, M., Zio, E., and Podofillini, L., 2002. Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliability Engineering & System Safety, 77(2), pp.151-
Chootinan, P., Chen, A., Horrocks, M., and Bolling, D., 2006. A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach. Transportation Research Part A: Policy and Practice, 40(9), pp. 725-743.
Jiejuan, T., Dingyuan, and Dazhi, X., 2004. A genetic algorithm solution for a nuclear power plant risk–cost maintenance model. Nuclear Engineering and Design, 229(1), pp. 81-89
Tan, S., and Kramer, A., 1997. A general framework for preventive maintenance optimisation in chemical process operations. Computers and Chemical Engineering, 21(12), pp.1451-1469.
Simon, D., 2013. Evolutionary Optimization Algorithms: Biologically-Inspired and Population-Based Approaches to Computer Intelligence. Hoboken: Wiley.
Gülich Johann Friedrich. “Centrifugal Pumps”; Springer-Verlag Berlin Heidelberg; 2014.
SOC, 2016. Mechanical Analysis Group, Rotating maintenance records, Gas plant, Sirte Oil Company (SOC).