Using Instrumented Revenue Vehicles to Inspect Track Integrity and Rolling Stock Performance in a Passenger Network During Peak Times

Lingamanaik, Siva Naidoo; Thompson, Cameron; Nadarajah, Nithurshan; Ravitharan, Ravi; Widyastuti, Hera; Chiu, Wing Kong

doi:10.1016/j.proeng.2017.04.504

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…This means that these data sets are usually not continuous nor do they have a small enough time interval between readings [28]. Rather than using dedicated inspection cars, the measurement platform can be mounted in standard trains with daily service [29]. The continuous measurement from every train provides a stream of data that can be used in the data-driven analysis.…”

Section: Data Acquisition In Railway Track Engineeringmentioning

confidence: 99%

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

et al. 2020

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Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

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Section: Data Acquisition In Railway Track Engineeringmentioning

confidence: 99%

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

et al. 2020

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“…Lintas timur Pulau Jawa yaitu jalan rel yang menghubungkan Kota Surabaya dengan kota-kota di wilayah timur Pulau Jawa (Jember, Banyuwangi) [3]. Jalan rel merupakan bagian dari prasarana kereta api yang ikut berperan dalam menentukan keselamatan, keamanan, kenyamanan, dan ketepatan waktu perjalanan [4], maka pemerintah mencetuskan ide pembangunan kereta api semicepat di lintas Utara Pulau Jawa dengan kecepatan maksimal hingga 160 km/jam dan kecepatan rata-rata 145 km/jam. Dengan adanya peningkatan kecepatan hingga 160 km/jam, maka perlu adanya identifikasi dan solusi atas kebutuhan kebutuhan pemeliharaan track sangat penting untuk meningkatkan kinerja jaringan, mencapai operasi jaringan yang lebih aman dan mengurangi atau mencegah kerusakan dalam jangka pendek dan panjang [5] mengingat track merupakan bagian yang penting dalam perjalanan kereta api dan juga aset bernilai tinggi.…”

Section: Pendahuluanunclassified

Penentuan Rekomendasi Standar Track Quality Index (TQI) untuk Kereta Semicepat di Indonesia (Studi Kasus : Surabaya - Cepu)

Lubis

Widyastuti

2020

JITS

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The growth of transportation sector is inevitable things that appear as public transportation user needs, commonly they need safe, reliable and punctual transportation. Medium speed train on Java's north line with maximum speed 160 km/h on existing track emerge as a solution that issued by local Indonesian government. Increasing speed on existing track must be considered by increasing TQI value. TQI is track quality index that consists of : track gauge, cant, longitudinal level, and lateral level. The recent value of TQI on existing track along Java's north line is 20, and Surabaya-Cepu line was taken as object of this examination. By using simple linear regression and multi linear regression that involved 4 parameters and speed as an approach, the value of TQI that obtained as recommendation on maximum speed 160 km/h.

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“…In the last two decades, the train has become one of the most popular modes of land transportation in Indonesia, appealing to people of all socioeconomic backgrounds. As a mode of transportation, trains are quite efficient as they have used less land per passenger as well as environmental impact compared with other modes of land transport [1]. Therefore, improving the service quality should be considered to maintain passenger satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of speed limit on the railway track using track quality index and multibody dynamics simulation

Widyastuti¹

2023

Materials Research Proceedings

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Abstract. The performance of track conditions must be thoroughly assessed to ensure the safe operation of the train that travels through the track. The Track Quality Index (TQI) is used to determine the condition of the railway track. The TQI value is a statistical summary of track geometry parameters measured over a specified track length. There are several methods used to analyze TQI including the Indonesian Railway standard (PT. KAI). The KAI’s analysis method of the TQI is a sum up of four parameters which are alignment, longitudinal level, cross-level, and track gauge. Moreover, TQI is used to determine the speed limit allowed for the train to pass through the track. A multibody dynamic system simulation was recently performed as a reference to compare track quality assessments based on driving safety and vertical loads on rails, in which one of the outputs is the speed limit. This result shows that the speed limit based on the multibody system is slightly lower at certain segments compared to the TQI.

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Using Instrumented Revenue Vehicles to Inspect Track Integrity and Rolling Stock Performance in a Passenger Network During Peak Times

Cited by 12 publications

References 9 publications

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

Penentuan Rekomendasi Standar Track Quality Index (TQI) untuk Kereta Semicepat di Indonesia (Studi Kasus : Surabaya - Cepu)

Prediction of speed limit on the railway track using track quality index and multibody dynamics simulation

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