Predictive Maintenance Based On Machine Learning In Public Transportation Vehicles

Year 2022, Volume: 4 Issue: 1, 89 - 98, 29.04.2022

Özlem Güven , Hasan Şahin

https://doi.org/10.46387/bjesr.1093519

Abstract

Predictive maintenance is an approach to prevent failure in a system by estimating the time of failure before a mechanical component fails, so that the maintenance decision can be properly planned. In the public transport industry, whose efficiency is heavily dependent on equipment, anticipating breakdowns is vital. In this study, predictive maintenance work was carried out in order to minimize problems such as malfunctions in public transport vehicles, stopping the voyage, delaying the journey and causing an accident due to unplanned breakdowns. Based on instant vehicle health data obtained from IoT sensors, classification techniques were run in machine learning. For maintenance planning, the probability of vehicles being normal and malfunctioning was examined with fuzzy logic and fuzzy outputs were obtained at maintenance speed. With the predictive maintenance approach applied to the data of the study taken from the vehicles, almost all of the faults in the vehicles could be detected.

Keywords

Maintenance, Predictive Maintenance, Machine Learning, Internet of Things, Smart Public Transportation

Toplu Taşıma Araçlarında Makine Öğrenmesine Dayalı Kestirimci Bakım

Abstract

Kestirimci bakım, bir sistemin mekanik bir bileşeninde arıza oluşmadan önce arıza zamanını tahmin ederek bakım kararının doğru planlanabilmesi için arızanın önlenmesini sağlayan bir yaklaşımdır. Verimliliği büyük ölçüde donanıma bağlı olan toplu taşıma sektöründe, arızaların önceden tahmin edilmesi hayati önem taşır. Bu çalışmada toplu taşıma araçlarında meydana gelen arıza, seferin durdurulması, yolculuğun ertelenmesi ve plansız arızalar nedeniyle kazaya neden olması gibi sorunları en aza indirmek için kestirimci bakım çalışması yapılmıştır. IoT sensörlerinden elde edilen anlık araç sağlığı verilerine dayalı olarak makine öğrenmesinde sınıflandırma teknikleri çalıştırılmıştır. Bakım planlaması için araçların normal ve arızalı olma olasılığı bulanık mantıkla incelenmiş ve bakım hızında bulanık çıktılar elde edilmiştir. Araçlardan alınan çalışmanın verilerine uygulanan kestirimci bakım yaklaşımı ile araçlardaki arızaların neredeyse tümü tespit edilebilmiştir.

Keywords

Bakım, Kestirimci Bakım, Makine Öğrenmesi, Nesnelerin İnterneti, Akıllı Toplu Taşıma Sistemleri

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