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All Journal Rekayasa Mesin Eduvest - Journal of Universal Studies
Virdyawan, Vani
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Aerospace Engineering Computer Science & IT Health Professions Mechanical Engineering Neuroscience Social Sciences

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Classification of Vertical and Lateral Track Irregularities using GoogleNet from Gramian Angular Summation Field Encoding Pratama, Gemuruh Geo; Virdyawan, Vani; Handoko, Yunendar Aryo
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
RELIABILITY EVALUATION OF OVERHEAD POWER TRANSMISSION SYSTEMS TO SUPPORT ELECTRIC RAILWAY OPERATIONS IN THE JABODETABEK AREA Prasetiyo, Edwin Rozzaq; Virdyawan, Vani; Rachmildha, Tri Desmana
Jurnal Rekayasa Mesin Vol. 16 No. 2 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i2.1851

Abstract

Overhead catenary system (OCS) is a vital infrastructure for the operation of electric railway trains as it delivers electrical power from the traction substation to the train pantograph. Increasing train trips in urban areas imposes additional mechanical and electrical loads on OCS components, thus demanding more frequent and accurate maintenance. This study introduces a risk-based reliability evaluation approach for critical OCS components by integrating risk matrix analysis with Anderson-Darling distribution fitting for failure and repair data. The methodology enables precise reliability and availability assessment of each component, comparing results to the operational targets. Findings show that contact wire, messenger wire, and hanger have reliability values below the company’s standard, whereas feeder wire and transmission poles exceed the targets. The proposed approach provides a data-driven framework to support condition-based maintenance planning and improve system readiness.
Co-Authors Handoko, Yunendar Aryo Prasetiyo, Edwin Rozzaq Pratama, Gemuruh Geo Tri Desmana Rachmildha, Tri Desmana