BAYES AĞLARI KULLANARAK HATA TEMELLİ BAKIM PLANLAMASI: HİDROLİK TÜRBİNDE BİR VAKA ÇALIŞMASI

Year 2022, Volume: 11 Issue: 1, 301 - 312, 24.03.2022

Gökhan Kahraman , Melih Yücesan

https://doi.org/10.17798/bitlisfen.1022757

Cited By: 1

Abstract

Enerji sektöründe mevcut altyapıların değerlendirilmesi dünya için büyük ekonomik öneme sahiptir. Hidroelektrik santrallerin enerji üretim ömrünün uzatılması, ekipmanların bakımı ve yenilenmesi ile ilgili mantıklı kararlara bağlıdır. Bu amaçla, türbin arızasını hesaplamak için hidrolik türbindeki arızaları değerlendirmek için bir Bayes ağı (BN) uygulanmıştır. Sistemin işleyişini etkileyecek kırk altı düğüm tespit edilmiştir. En yüksek arka olasılıklı arızalar için önleyici tedbirler oluşturulmuştur. Dört farklı durum oluşturularak arıza olasılıkları ve ana arızanın değişimi hesaplanmıştır. Her durumda ne kadar tasarruf yapılabileceği bakımla belirlenir. Önerilen bu çerçeve, hidroelektrik santral operatörleri için bakım stratejilerinin belirlenmesinde yol gösterici olacaktır.

Keywords

Bayes Ağı, hidrolik türbin, bakım

FAILURE-BASED MAINTENANCE PLANNING USING BAYESIAN NETWORKS: A CASE STUDY HYDRAULIC TURBINE

Abstract

The assessment of existing infrastructures in the energy sector is of great economic importance for the world. The extension of the power generation life of hydroelectric power plants depends on logical decisions regarding the maintenance and renewal of the equipment. For this purpose, a Bayesian network (BN) has been applied to evaluate the failures in the hydraulic turbine to calculate the failure of the turbine. Forty-six nodes have been identified that will affect the operation of the system. Preventive measures have been established for failures with the highest posterior probability. By creating four different cases, failure probabilities and the change of the main fault have been calculated. How much savings could be made in each case is determined with the maintenance. This proposed framework will be guided in determining the maintenance strategies for hydroelectric power plant operators.

Keywords

Bayesian network, hydraulic turbine, maintenance

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• (URL-1) https://www.microsoft.com/en-us/download/details.aspx?id=52299