Power plants are the large-scale production facilities with the main purpose of realizing uninterrupted, reliable, efficient, economic and environmentally friendly energy generation. Maintenance is one of the critical factors in achieving these comprehensive goals, which are called as sustainable energy supply. The maintenance processes carried out in order to ensure sustainable energy supply in the power plants should be managed due to the costs arising from time requirement, the use of material and labor, and the loss of generation. In this respect, it is critical that the fault dates are forecasted, and maintenance is performed without failure in power plants consisting of thousands of equipment. In this context in this study, the maintenance planning problem for equipment with high criticality level is handled in one of the large-scale hydroelectric power plants that meet the quintile of Turkey’s energy demand as of the end of 2018. In the first stage, the evaluation criteria determined by the power plant experts are weighted by the Analytical Hierarchy Process (AHP), which is an accepted method in the literature, in order to determine the criticality levels of the equipment in terms of power plant at the next stage. In order to obtain the final priority ranking of the equipment in terms of power plant within the scope of these weights, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used because of its advantages compared to other outranking algorithms. As a result of this solution, for the 14 main equipment groups with the highest criticality level determined on the basis of the power plant, periods between two breakdowns are estimated, and maintenance planning is performed based on these periods. In the estimation phase, an artificial neural network (ANN) model has been established by using 11-years fault data for selected equipment groups and the probable fault dates are estimated by considering a production facility as a system without considering the sector for the first time in the literature. With the plan including the maintenance activities that will be carried out before the determined breakdown dates, increasing the generation efficiency, extending the economic life of the power plant, minimizing the generation costs, maximizing the plant availability rate and maximizing profit are aimed. The maintenance plan is implemented for 2 years in the power plant and the unit shutdowns resulting from the selected equipment groups are not met and the mentioned goals are reached.
Bakım, üretim tesislerinin kesintisiz, kalite d üzeyi yüksek, ekonomik, verimli, güvenilir ve çevreye duyarlı üretim yapması olarak tanımlanan sürdürülebilirlik hedefine üst düzeyde katkı sağlayan bir prosestir. Bu önemli prosesin en önemli aşamalarının başında bakım planlaması gelmektedir ve bu fazın ilk ve vazgeçilmez aşaması ise bakım strateji seçimidir. Bakım proseslerinin üretim duruşu, malzeme, zaman ve iş gücü gereksinimi nedeniyle önemli maliyetler doğurması düşünüldüğünde, özellikle kritik ekipmanlara uygun bakım
Covid-19 Takibinde Giyilebilir Sağlık Teknolojilerinin ÇKKV Yöntemleri ile Değerlendirilmesi Evaluation of Wearable Health Technologies with MCDM Methods in Covid-19 Monitoring Önemli noktalar (Highlights) Covid-19 hastalarının giyilebilir sağlık teknolojileri ile uzaktan takibi. / Monitoring Covid-19 patients remotely with wearable health technologies. Uzaktan takip sonucu bulaşın azalması. /Reduction of transmission as a result of remote monitoring. Doktor-hasta temasının en aza indirgenmesi. /Minimization of doctor-patient contact.
People have struggled with many infectious diseases throughout history. Today, the Covid-19 is being fought. One of the most important things for people who have or are at risk of getting Covid-19 is social isolation. Many countries resort to different ways to ensure social isolation. For this, remote patient monitoring systems have been developed. In this study, the problem of the selection of Covid-19 remote patient monitoring systems is discussed. Seven Wearable Health Technology (WHT) products were evaluated with a total of 10 criteria, including the important symptoms used in the patient tracking systems. The weights of 10 criteria determined by the Analytical Hierarchy Process (AHP) method were calculated, and these weights were used in the solution of The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) methods. WHT products were compared. As a result, the most appropriate patient follow-up system was determined. This study generates differences in terms of evaluating seven different products and ten criteria in total with MCDM methods. A more comprehensive evaluation has been made in the literature than the studies in this field.
Maintenance scheduling for different types of equipment for the first time in the literature Integrated maintenance scheduling and generation estimation Maintenance scheduling for five different types of preventive maintenance for the first time in the literature Figure A. Application steps Purpose:In this study, by using this data a 1-year maintenance schedule is obtained for the critical equipment groups that should be implemented the periodic maintenance strategies with 5 different contents and frequency via the proposed hybrid methodology. In the first step of this study, which uses the combination of Artificial Neural Network (ANN) and Integer Programming (IP), 1-year generation estimation of the power plant is realized by ANN method and operation and maintenance hours of the power plant are calculated from this forecasted data. These calculated periods are included in the proposed maintenance scheduling model which reflects all the requirements and realities of the hydroelectric power plant where the application is carried out and a feasible maintenance schedule is obtained. This study is the first in the literature in terms of handling multiple equipment groups in power plants, integrating the annual operating hours of the power plant into the maintenance scheduling problem in a consistent manner with real life, and providing an effective and applicable annual maintenance schedule that is compatible with the operation of the power plant. Theory and Methods:Artificial Neural Network and Integer Programming methods were used in this study. Results:As a result of the study, five different preventive maintenance of the critical equipment of the plant have been scheduled. The contributions of this study to the literature can be summarized in below: While there are often studies creating the maintenance schedules for a single equipment in the literature, in this study, more than one electrical equipment group is evaluated in a power plant and a mathematical model is presented considering the specific maintenance characteristics of each equipment group. In this study, considering the operating conditions of the power plant an applicable maintenance schedule is obtained by taking into account the 5 different periods as weekly, monthly, quarterly, 6 months and yearly. 1-year generation forecast for the power plant by using the 9-years real data of 2010-2018 in proposed ANN model, and calculating the operating and maintenance capacities (hours) for the planning period in the maintenance scheduling problem are two stages of a unique application in the literature. In addition, the integration of generation and maintenance capacities of the power plant obtained by proposed ANN model into the proposed maintenance scheduling model which is generated by using IP has enabled the combination of ANN and IP methods for the first time in the literature. It is designed as a real-life application of working with real data taken from a hydroelectric power plant in Turkey stands out as a feature which is not considered in mo...
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