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

Home Page
About
Submit A Journal
Submit A Conference
Submit Paper/Book
- Submit a Preprint
- Submit a Book
Contact

Firat University Journal of Experimental and Computational Engineering
Cilt: 4 Sayı: 1
Fault Detection in Steel Belts of Tires Using Magnetic Sensors and Different Deep Learning Models

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

Pages : 85-99

View : 31 | Download : 28

Publication Date : 2025-02-18

Article Type : Research Paper

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 : Derin öğrenme, Çelik kayışlar, Manyetik sensör, Arıza tespiti

ORIGINAL ARTICLE URL

VIEW PAPER (PDF)

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.