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All Journal International Journal of Electrical and Computer Engineering Rekayasa Mesin Ge-STRAM: Jurnal Perencanaan dan Rekayasa Sipil Indonesian Journal of Electrical Engineering and Computer Science Eduvest - Journal of Universal Studies
Vani Virdyawan
Institut Teknologi Bandung
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Health Professions Mechanical Engineering Neuroscience Social Sciences Transportation

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Overview Of RAMS Analysis and ITS Implementation in Railway Vehicles Muhamad Abdurrochman; Rachman Setiawan; Vani Virdyawan
Ge-STRAM: Jurnal Perencanaan dan Rekayasa Sipil Vol. 7 No. 2: September 2024
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/jprs.v7i2.8751

Abstract

Reliability, availability, maintainability, and safety (RAMS) analysis, as a tool to ensure performance, safety, and cost-effectiveness throughout the system’s life cycle, has been widely applied in various industries, including the railway industry, specifically railway vehicles. This article provides an overview of various studies exploring the implementation of RAMS analysis in railway vehicles. This literature review consists of an introduction, method, RAMS analysis, results and discussion, and conclusion. Several studies on the implementation of RAMS analysis in railway vehicles are presented and explained, and key points from each study are discussed to provide a comprehensive overview of the implementation of RAMS analysis in railway vehicles. In general, the benefits of the implementation of RAMS analysis are enhanced performance (reliability, availability, and maintainability), safety, and cost-effectiveness of railway vehicles throughout their life cycle. However, many studies only focus on one or a few aspects of performance, safety, or cost-effectiveness without comprehensive discussions of all these aspects. Additionally, many studies only concentrate on the operational phase, including maintenance and repair, without a complete discussion of the entire life cycle from concept to disposal stage.
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.
USULAN KEBIJAKAN PEMERIKSAAN PADA GARDU TRAKSI LRT PALEMBANG BERDASARKAN ANALISIS NILAI KEANDALAN DAN KETERSEDIAAN Putra, Chandra Dwi; Virdyawan , Vani; Rachmilda , Tri Desmana; Suweca, I Wayan
Jurnal Rekayasa Mesin Vol. 16 No. 1 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i1.1853

Abstract

The Palembang LRT, the first light rail transportation system in Indonesia, operates at the traction substation. This research aims to propose an optimal inspection policy based on reliability and availability values. The study found that the reliability of the 750 V output DC cubicle sub-equipment is 84.01%, the feeder is 97.22%, the MV switchgear 20 kV AC is 98.60%, and the power supply controller (SCADA) is 94.04%. The availability value of the 750 V output DC cubicle sub-equipment is 99.99%, the feeder is 99.98%, the MV switchgear 20 kV AC is 99.99%, and the SCADA is 99.97%. The inspection time interval with a reliability target of 90% for the 750 V output DC cubicle sub-equipment is 181 hours, with the feeder sub-equipment having intervals of 1256 hours, MV switchgear 20 kV AC sub-equipment every 2513 hours, and the SCADA sub-equipment every 599 hours.
Parameter identification and continuous friction modelling of a brushed DC motor Ahmad’Abdan Syakuro; Vani Virdyawan; Sri Raharno; Indrawanto Indrawanto; Tegoeh Tjahjowidodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp699-707

Abstract

Designing high-performance control systems for brushed DC motors is often hindered by the lack of comprehensive dynamic parameters in manufacturer datasheets, particularly for low-cost DC motors. In addition to the parameter identification method, this study also introduces a continuously differentiable friction model incorporating Coulomb and viscous-like behaviors using a hyperbolic tangent function. The electrical and mechanical parameters of an RS-775 motor were identified using standard laboratory tools and the MATLAB system identification toolbox. The proposed model was validated against experimental data under square wave and sinusoidal inputs, achieving a position prediction error of less than 5% and capturing complex dynamic behaviors. The results demonstrate that this accessible identification approach provides a sufficiently accurate dynamic model for educational and industrial robotics applications, offering a superior alternative to trial-and-error tuning.
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
Design and manufacture of a self-balancing system for two-wheeled vehicle models using a reaction wheel Indrawanto Indrawanto; Yuzar Arigi; Vani Virdyawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1853-1866

Abstract

Motorbikes are a popular mode of transportation in Indonesia and are agile in maneuvering on roads with heavy traffic. The increasing use of motorbikes has triggered many accidents. This paper discusses the design, manufacture, and control of a self-balancing system for a two-wheeled vehicle model to improve driving safety. The self-balancing system designed uses a reaction wheel. The system architecture consists of a microcontroller board, a DC motor, a gyroscope, a reaction wheel, and a two-wheel vehicle model. The dimensions of the reaction wheel are optimized between the mass and the moment of inertia to make it possible to self-balance the model from a certain initial angle. The controller is designed based on the state space model with a feedback linear-quadratic regulator controller. The matrix weighting values are selected using Bryson’s rules method. Experimental results show that the self-balancing system can work well for the two-wheel vehicle model.
Co-Authors Ahmad’Abdan Syakuro Gemuruh Geo Pratama I Wayan Suweca Indrawanto Indrawanto Indrawanto Muhamad Abdurrochman Prasetiyo, Edwin Rozzaq Putra, Chandra Dwi Rachman Setiawan Rachmilda , Tri Desmana Raharno, Sri Tegoeh Tjahjowidodo Tri Desmana Rachmildha, Tri Desmana Yunendar Aryo Handoko Yuzar Arigi