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All Journal Rotasi Rekayasa Mesin Journal of Engineering and Technological Sciences JURNAL DINAMIKA VOKASIONAL TEKNIK MESIN Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Teknologi dan Manajemen Jurnal Nasional Aplikasi Mekatronika, Otomasi dan Robot Industri (AMORI) Eduvest - Journal of Universal Studies Jurnal Penelitian Transportasi Darat
Yunendar Aryo Handoko
Institut Teknologi Bandung
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mechanical Engineering Neuroscience Public Health Social Sciences Transportation Other

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Classification of Vertical and Lateral Track Irregularities using GoogleNet from Gramian Angular Summation Field Encoding Gemuruh Geo Pratama; Vani Virdyawan; Yunendar Aryo Handoko
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.50895

Abstract

The ability to classify track conditions has become a critical issue in the railway industry, as delayed detection or unaddressed adverse track conditions can profoundly impact railway safety. Current track maintenance primarily relies on manual inspections and specialized monitoring vehicles, which are constrained by their inspection frequency. Deploying models that correlate vehicle dynamic responses with track conditions in in-service trains could significantly enhance fault detection. However, existing studies utilizing machine learning approaches are notably limited in capturing complex time-series information from vehicle dynamic responses, especially when the data are derived from real measurements rather than simulations. To address these challenges, we propose the application of GoogleNet and Gramian Angular Summation Field (GASF) transformation for classifying track conditions using vehicle dynamic responses. For comparison, we will demonstrate the limitations of traditional machine learning approaches, specifically Logistic Regression and XGBoost, where only the standard deviation and peak value are extracted as features. Subsequently, we propose our approach using the GoogleNet architecture, combined with GASF to transform the time-series data into image representations. Our proposed model achieves high accuracy, in classifying vertical and lateral track conditions, significantly outperforming the machine learning model. The results of this study demonstrate that our proposed method can learn complex nonlinear features, and make accurate classifications. Additionally, the study highlights the inability of the machine learning model, to classify track conditions accurately, and provides evidence that standard deviation and peak value are insufficient as features for complex systems like vehicle dynamic responses
Co-Authors Andy Saputro Arfiansyah, Oky Arfiansyah, Oky Eki Dwi Juliansyah Putra Gemuruh Geo Pratama Gunawan , Leonardo Gunawan I Wayan Suweca Indria Herman Kartikaningtyas, Dela Safitri Kusuma Abdillah Maulana, Mochamad Irvan Prasidya Wikaranadhi Rachmad Indrakusuma Raharno, Sri Ramadhan, Fakih Ilham Tria Mariz Arief Vani Virdyawan Yusran, Yasser