Boru hatlarında çizge evrişimsel ağlar yöntemi (GCN) ile arıza tespiti

Year 2025, Volume: 40 Issue: 1, 673 - 684, 16.08.2024

Ersin Şahin , Hüseyin Yüce

https://doi.org/10.17341/gazimmfd.1306916

Abstract

Boru hatları petrol ve doğalgaz gibi enerji kaynaklarının taşınmasından, su kaynaklarının iletilmesi ve dağıtılmasına kadar çok geniş bir alanda kullanım alanına sahiptir. Boru hatlarından sızan petrol ve gaz akışkanları çevreye ciddi zararlar vermektedir. Boru hatlarındaki arızaların doğru bir şekilde tespit edilmesi, ekonomik kayıpların etkilerinden kaçınmak ve çevreyi korumak için önemlidir. Bu çalışmada su akışkanına sahip boru hatları çizgeler (graf) ile temsil edilmiştir. Boru hatlarında sızıntı ve tıkanma durumlarının tespiti için çizge temelli makine öğrenmesi algoritması GCN kullanılmıştır. Deneysel yöntemler kullanılarak GCN algoritması için gerekli olan veriler (basınç verileri), beş farklı senaryo oluşturularak toplanmış ve iki adet veri seti oluşturulmuştur. GCN algoritmasından elde edilen arıza tespit performans değeri diğer çizge makine öğrenmesi algoritmaları; RGCN, HinSAGE ve GraphSAGE ’nin performansları ile karşılaştırılmıştır. Bu çalışmada GCN modelinin performansı diğer algoritmalara göre daha yüksek bulunmuştur. Literatür incelendiğinde makine öğrenmesi algoritmaları kullanılarak boru hatlarında arıza teşhisi için doğruluk oranları %78,51 ile %99 değerleri arasında tespit edilmiştir. Bu çalışmada, GCN, GraphSAGE, HinSAGE ve RGCN algoritmalarının sırasıyla %0,91, %0,90, %0,87, %0,89 doğruluk oranları ile boru hatlarında arıza tespiti yaptıkları bulgusuna varılmıştır. Çizge temelli algoritmaların performanslarının kıyaslanması için klasik makine öğrenmesi SVM modeli kullanılmıştır. Algoritmaların performansları literatür ile karşılaştırıldığında sonuçların literatür ortalamasının üstünde olduğu görülmektedir.

Keywords

Çizge evrişimli ağlar, Arıza tespiti, Boru hatları

Supporting Institution

Marmara Üniversitesi

Project Number

FDK-2023-10459

Thanks

Bu çalışma, Marmara Üniversitesi tarafından FDK-2023-10459 numaralı bilimsel araştırma projesi ile desteklenmiştir. Çalışmanın deneysel aşamalarında Marmara Üniversitesi, Teknoloji Fakültesi Mekatronik Mühendisliği Arıza Teşhis Laboratuvarında bulunan deney seti kullanılmıştır. Bu imkanları sağlayan Marmara Üniversitesine teşekkürü bir borç bilirim.

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