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Sistem güvenilirliğinde optimizasyon

Authors: Mert, Şefika Büşra;

Sistem güvenilirliğinde optimizasyon

Abstract

Today, thanks to the complex structure of the systems and the advancing technology, the correct operation and controllability of the systems has made the issue of reliability important. Many applications have been carried out in engineering, from the proper functioning of the functions expected from the system to minimizing the errors in the system and the sub-components that make up the system. Since the systems consist of sub-components interacting with each other, the reliability of the components in the system also affects the overall reliability of the system. While the factors that have a positive effect on the system can be improved by using optimization methods according to the purpose of the problem, the negative factors should be minimized or eliminated. When using these methods, the use of meta-heuristic algorithms may be preferred to find the closest solutions to the targeted purpose. Teaching-Learning Based Optimization (TLBO), Genetic Algorithms (GA), can be given as examples to these algorithms inspired by real life. Teaching-Learning Based Optimization (TLBO) algorithms are an optimization algorithm developed to improve the current problem based on teacher-student relationships and interactions between students. In this study, TLBO algorithm is considered to optimize the reliability of a lineer k-out-of-n: F systems and lineer consecutive k-out-of-n: F system, and it is aimed to obtain the reliability of the components that give the highest value of system reliability. In addition, results were obtained with the Genetic Algorithm for the system in question and compared with the results obtained from TLBO.

Günümüzde sistemlerin karmaşık yapısı ve ilerleyen teknoloji sayesinde sistemlerin doğru çalışabilmesi ve kontrol edilebilir durumda olması güvenilirlik konusunu önemli bir hale getirmiştir. Sistemler birbiri ile etkileşim halinde bulunan alt bileşenlerden oluştuğu için sistemde bulunan bileşenlerin güvenilirlikleri de sistemin genel güvenilirliği üzerinde etkili olur. Sistem üzerinde olumlu etkiye sahip olan faktörler problemin amacına göre optimizasyon yöntemleri kullanılarak iyileştirilebilirken, olumsuz faktörlerin de en aza indirilmesi veya ortadan kaldırılması gerekir. Bu yöntemler kullanılırken hedeflenen amaca en yakın çözümleri bulabilmek için meta-sezgisel algoritmaların kullanımı tercih edilebilir. Gerçek hayattan esinlenerek oluşturulan bu algoritmalara Öğretme-Öğrenme Esaslı Optimizasyon algoritması (Teaching-Learning Based Optimization-TLBO), Genetik Algoritmalar (GA) örnek olarak gösterilebilir. Öğretme-Öğrenme Esaslı Optimizasyon (TLBO) algoritmaları, öğretmen-öğrenci ilişkilerini ve öğrencilerin kendi aralarındaki etkileşimlerinden yola çıkarak mevcut problemin iyileştirilmesi üzerine geliştirilen bir optimizasyon algoritmasıdır. Bu çalışmada doğrusal n’den k çıkışlı ve doğrusal ardışık n’den k çıkışlı F sistemlerin güvenilirliğinin optimize edilmesi için TLBO algoritması ele alınmış ve bununla birlikte sistem güvenilirliğinin en yüksek değerini veren bileşenlerin güvenilirliklerinin elde edilmesi amaçlanmıştır. Ayrıca söz konusu sistem için Genetik Algoritma ile de sonuçlar elde edilmiş olup, TLBO’dan elde edilen sonuçlar ile karşılaştırılmıştır.

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Turkey
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Keywords

n’den k Çıkışlı Sistemler, Genetic Algorithm, Sistem Güvenilirliği Optimizasyonu, Genetik Algoritma, k-out-of-n Systems, Teaching-Learning Based Optimization Algorithm, System Reliability Optimization, Öğretme-Öğrenme Esaslı Optimizasyon Algoritması

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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