Condition-based Maintenance: Determination of optimal deterioration levels to perform preventive activities by using a multi-objective evolutionary algorithm
Several authors insist on the fact that maintenance is a key activity in the manufacturing industry, because of its economic consequences. Within maintenance, Condition-Based Maintenance programs can provide significant advantages to industrial plants. This paper is focused on the problem of Condition-Based Maintenance optimization in an industrial environment, with the objective of determining both the critical age level to perform preventive maintenance activities and the amount of this type of activities to be executed before upgrading or substituting components. For this purpose, a mathematical model who jointly considers the evolution in quality and production speed along with condition based, corrective and preventive maintenance is presented. The cost and profit optimization process using a Multi-Objective Evolutionary Algorithm is applied to an industrial case.
How to Cite
Condition Based Maintenance, industrial case, genetic algorithms
Barbera, F., Schneider, H., & Watson, E. (1999). A condition based maintenance model for a two-unit series system. European Journal of Operational Research, 116, 281–290.
Cassady, C. R., Bowden, R. O., & Pohl, E. A. (2000). Combining preventive maintenance and statistical process control: a preliminary investigation. IIE Transactions, 32, 471–478.
Das, T. K., & Sarkar, S. (1999). Optimal preventive maintenance in a production inventory system. IIE Transactions, 31, 537–551.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Grall, A., Berenguer, C., & Dieulle, L. (2002). A condition based maintenance policy for stochastically deteriorating systems. Reliability Engineering and System Safety, 76, 167–180.
Kuo, Y. (2006). Optimal adaptive control policy for joint machine maintenance and product quality control. European Journal of Operational Research, 171, 586–597.
Linderman, K., McKone-Sweet, K. E., & Anderson, J. C. (2005). An integrated systems approach to process control and maintenance. European Journal of Operational Research, 164, 324–340.
Malik, M. A. K. (1979). Reliable preventive maintenance scheduling,. AIIE Transactions, 11, 221–228.
Marhadi, K. (2015). Automatic threshold setting and its uncertainty quantification in wind turbine condition monitoring system. IJPHM, 6(005), 1–15.
Martorell, S., Sanchez, A., & Serradell, V. (1998). Residual life management of safety-related equipment considering maintenance and working conditions. Esrel 1998 (2, 889–896), June 16-19, Tronheirn, Norway.
Mehairjan, R. P. Y., Zhuang, Q., Djairam, D., & Smit, J. J. (2015). Upcoming Role of Condition Monitoring in Risk-Based Asset Management for the Power Sector. In Tse, P.W., Mathew, J., Wong, K., & Ko, L.C.N. (Eds.), Engineering Asset Management - Systems, Professional Practices and Certification (863–875). Switzerland: Springer International.
Mjema, E. A. M. (2002). An analysis of personnel capacity requirement in the maintenance department by using a simulation method. Journal of Quality in Maintenance Engineering, 8(3), 253–273.
Oyarbide-Zubillaga, A., Sanchez, A., & Goti, A. (2007). Determination of the optimal maintenance frequency for a system composed by N-machines by using Discrete Event Simulation and Genetic Algorithms. ESREL 2007 (1, 297–304). June 25-27, Stavanger, Norway.
Oyarbide-Zubillaga, A., Goti, A., Sanchez, A. (2008). Preventive maintenance optimization of multi-equipment manufacturing systems by combining discrete event simulation and multiobjective evolutionary algorithms. Production planning & Control, 19(4), 297–304.
Scarf, P. A. (1997). On the application of mathematical models in maintenance. European Journal of Operational Research, 99, 493–506.
Simmons Ivy, J., & Black Nembhard, H. (2005). A modeling approach to maintenance decisions using statistical quality control and optimization. Quality and Reliability Engineering International, 21, 355–366.
Valčuha, Š., Goti, A., Úradníček, J., & Navarro, I. (2011). Multi-equipment condition based maintenance optimization by multi- objective genetic algorithm. Journal of Achievements in Materials and Manufacturing Engineering, 2(1), 188–193.
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