Otonom Taşıyıcı Araçlardaki Hataların Teşhisi için Evrişimli Sinir Ağları Tabanlı Çoklu Heterojen Sensörlerin Füzyon Yöntemi

Year 2022, Volume 34, Issue 1, 203 - 213, 20.03.2022

Sergen AŞIK Emine DENİZ Eyüp ÇİNAR

https://doi.org/10.35234/fumbd.1003341

Abstract

Bu çalışma, çok sensörlü veri füzyonu ve darboğaz katmanı ile optimize edilmiş evrişimli sinir ağı tabanlı otonom taşıyıcı araçlar için yeni bir hata tespit yöntemi sunmaktadır. Daha zengin öznitelikler elde etmek için tek sensörden gelen sinyal verileri yerine çoklu heterojen sensörlerden gelen sinyal verilerini resimlere dönüştüren bir dönüştürme yönteminden yararlanılmıştır. Çoklu heterojen sensör verilerinin dönüşümü ile elde edilen resimler kullanılarak yeni bir evrişimli sinir ağı oluşturulmuştur. Önerilen yaklaşımın etkinliğini doğrulamak için otonom taşıyıcı araç kullanılarak sensörlerden veriler toplanmış ve gerçek bir fiziksel ortamda çeşitli hata senaryoları oluşturulmuştur. Tek sensör kullanarak ortalama %85 ve çoklu heterojen sensör kullanılarak ortalama %93 hata tahmin doğruluğu elde edilmiştir. Sonuçlarda gösterildiği gibi önerilen evrişimli sinir ağı, daha yüksek tanıma doğruluğu elde etmektedir.

Keywords

Fault diagnosis, Convolutional neural networks, Multi-sensor data fusion, Autonomous transport vehicle

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