Gas Turbine Preventive Maintenance Optimization Using Genetic Algorithm

Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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RELI04_046

تاریخ نمایه سازی: 1 مرداد 1397

Abstract:

The tremendous impact of an optimized maintenanceprogram on system overall cost and reliability leadsvarious industrial managers and owners to seek anintelligent tool for maintenance decision making. Gasturbine industry is no exception, since it is of the mostexpensive and critical components in both power plantand oil and gas industries. In this paper an intelligentmaintenance optimization tool is developed based ongenetic algorithm. Genetic algorithm is a heuristicoptimization method in which genetic evolutionpatterns are employed. The algorithm has been used forsolving several optimization problems and its ability tofind optimized solutions makes it one of the most usedalgorithms. The main purpose of proposed algorithm isto make the balance between maintenance costs (i.e.direct and indirect) and down time cost whilemaintaining system availability on predefined level.Moreover, maintenance constraints such as taskinterval, maintenance duration are considered. Tohandle these constraints, new repair operators aredefined and applied in the proposed genetic algorithm,besides other crossover and mutation operators. Inorder to verify and validate the novel developedalgorithm, results of its implementation on a gasturbine case study are discussed. The case study is amaintenance optimization problem of Siemens SGT600gas turbine, comprised of seventeen components andtheir maintenance activities, two life wear patterns andfour production loss scenarios. Results of the optimizedsolution are compared with gas turbine conventionalmaintenance plan which is proved to have considerableimprovements. It is shown that an optimizedmaintenance plan would reduce outage time and alsoincrease the availability, which is mainly due togrouping maintenance activities. Besides, reduction intotal cost including maintenance costs and productionloss cost are of economic consequences of usingproposed algorithm. Total cost is reduced more than80% while availability is improved roughly 2%.

Authors

Fatemeh Moinian

RAM project manager, Middle-East Turbo Compressor. (Turbotec) Co

Hamed Sabouhi

RAM expert, Middle-East Turbo Compressor. (Turbotec) Co

Jafar Hushmand

System Analysis Division manager, Middle-East Turbo Compressor. (Turbotec) Co

Ahmad Hallaj

After-Sale Division manager, Middle-East Turbo Compressor. (Turbotec) Co