Fault Detection in Steel Belts of Tires Using Magnetic Sensors and Different Deep Learning Models

Year 2025, Volume: 4 Issue: 1, 85 - 99, 18.02.2025

Sercan Yalçın

https://doi.org/10.62520/fujece.1527246

Abstract

Tire failures pose significant safety risks, necessitating advanced inspection techniques. This research investigates the application of magnetic sensors and deep learning for detecting defects in steel belts of the tires. It was aim to develop a robust and accurate fault detection system by measuring magnetic field variations caused by defects. In this study, the magnetic image sensor circuit had been designed and then the images obtained from it have been classified as none, crack, and delamination type steel belt errors. Various deep learning models and their hybrid architectures, were explored and compared. Experimental results demonstrate that all models exhibit strong performance, with the Transformer model achieving the highest accuracy of 96.12%. The developed system offers a potential solution for improving tire safety and reducing maintenance costs in industries.

Keywords

Deep learning, Steel belts, Magnetic sensor, Fault detection

Ethical Statement

“There is no conflict of interest with any person/institution in the prepared article”

Manyetik Sensörler ve Farklı Derin Öğrenme Modelleri Kullanarak Lastiklerin Çelik Kayışlarındaki Arıza Tespiti

Abstract

Lastik arızaları önemli güvenlik riskleri oluşturur ve ileri seviye inceleme tekniklerini gerektirir. Bu araştırma, lastiklerin çelik kuşaklarındaki kusurları tespit etmek için manyetik sensörlerin ve derin öğrenmenin uygulanmasını incelemektedir. Kusurların neden olduğu manyetik alan değişimlerini yakalayarak, sağlam ve doğru bir arıza tespit sistemi geliştirilmesi amaçlanmaktadır. Bu çalışmada, manyetik görüntü sensör devresi tasarlanmış ve daha sonra ondan elde edilen görüntüler, hata olmayan, çatlak ve delaminasyon tipi çelik kuşak hataları olarak sınıflandırılmıştır. Çeşitli derin öğrenme modelleri ve bunların hibrit mimarileri araştırılmış ve karşılaştırılmıştır. Deneysel sonuçlar tüm modellerin güçlü bir performans sergilediğini, Transformatör modelinin %96.12'lik en yüksek doğruluğa ulaştığını göstermektedir. Geliştirilen sistem, endüstrilerde lastik güvenliğini iyileştirmek ve bakım maliyetlerini düşürmek için potansiyel bir çözüm sunmaktadır.

Keywords

Derin öğrenme, Çelik kayışlar, Manyetik sensör, Arıza tespiti

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