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All Journal Rotasi JURNAL DINAMIKA VOKASIONAL TEKNIK MESIN Syntax Literate: Jurnal Ilmiah Indonesia Eduvest - Journal of Universal Studies
Handoko, Yunendar Aryo
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Aerospace Engineering Humanities Computer Science & IT Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mechanical Engineering Neuroscience Social Sciences Other

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API-579 Fitness for Service Assessment of Pig Launcher Pipeline on Bulge Defect Condition: A Case Study Ramadhan, Fakih Ilham; Arief, Tria Mariz; Handoko, Yunendar Aryo; Saputro, Andy; Maulana, Mochamad Irvan
ROTASI Vol 26, No 3 (2024): VOLUME 26, NOMOR 3, JULI 2024
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/rotasi.26.3.37-43

Abstract

Fitness for service (FFS) is the standard assessment procedure to evaluate an operation's worthiness of static equipment due to its defect condition. FFS ensures a safety aspect of operation and produces a strategic action due to a defect occurring on an object. During a regular inspection activities at oil and gas plant, four bulges were found on a pig launcher pipeline. The FFS assessment following the API-579 standard was conducted as a case study. The initial assessment result shows that the bulge defect's geometrical aspect did not comply the required criteria. Then, a stress analysis assessment was conducted which showed that the safety factor and the elastic stress criteria were successfully fulfilled. This concludes that the stress occurred in the pipeline is still in its elastic deformation region. However, the failure of remaining strength factor acceptance criteria to be fulfilled shows that there was a degradation of pipeline capability to be loaded with the operating pressure. This whole assessment concludes that rerate remediation should be taken before the pipeline is reoperated by decreasing the maximum allowable operating pressure from 9,27 MPa to 8,22 MPa.
Review On Layout Optimization For Rollingstock Maintenance Depot Kartikaningtyas, Dela Safitri; Raharno, Sri; Handoko, Yunendar Aryo
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i1.17307

Abstract

This review paper discusses the importance of having an optimal layout for rollingstock maintenance depots to optimize rollingstock maintenance in improving maintenance efficiency and reducing maintenance costs while maintaining the availability and reliability of railway vehicles. The current problems in rollingstock maintenance depots faced by railway industry are limited land availability, limited storage space, material handling issues, and the lack of standards for train maintenance depot layouts. The paper also presents classification criteria and categories on layout planning and maintenance optimization approach for rollingstock maintenance depots based on various recent studies including the methods used and the results obtained. Finally, this review paper proposes guidelines for future research on rollingstock maintenance depots in Indonesia as decision-making that considers the economic factors of layout optimization and the implications for safety and the environment of maintenance activities. This could also help improve the company's reputation as well as prepare for future expansion
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
TRAIN WHEEL OUT-OF-ROUNDNESS (OOR) AND MACHINE LEARNING-VIBRATION BASED FAULT DIAGNOSIS: A REVIEW Yusran, Yasser; Suweca, I Wayan; Handoko, Yunendar Aryo
Jurnal Dinamika Vokasional Teknik Mesin Vol. 9 No. 1 (2024)
Publisher : Department of Mechanical Engineering Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/dinamika.v9i1.72682

Abstract

This article aims to give a complete review of previous and current research on numerous types of out-of-roundness (OOR) failures in train wheels, as well as diagnostic approaches based on machine learning and vibration data. The study provides a comprehensive overview of the current state of research by categorizing reviews into three primary domains: (1) types of OOR failures in train wheels, (2) fault diagnosis methodologies, and (3) the use of machine learning and vibration data to diagnose train wheel OOR failures. Initially, the study investigates the characteristics, causes, and consequences of railway wheel OOR failures, including their impact on vibrations. It then dives further into diagnostic methods, comparing the effectiveness of statistical methods to machine learning-based methods for diagnosing failures. Furthermore, the study addresses current advances in machine learning and vibration-based diagnostic methods to diagnose train wheel OOR failures, providing information on their applications and results. This article highlights that by utilizing machine learning methods with vibration data offers a promising way for accurately diagnosing OOR faults in train wheels and predicting their potential failure and remaining useful life, resulting to enhanced maintenance efficiency and less downtime.
Co-Authors Andy Saputro I Wayan Suweca Kartikaningtyas, Dela Safitri Maulana, Mochamad Irvan Pratama, Gemuruh Geo Raharno, Sri Ramadhan, Fakih Ilham Tria Mariz Arief Virdyawan, Vani Yusran, Yasser