Optimized railway track health monitoring system based on dynamic differential evolution algorithm

Chellaswamy, C.; Krishnasamy, Mei; Balaji, L.; Dhanalakshmi, A.; Ramesh, R.

doi:10.1016/j.measurement.2019.107332

Cited by 30 publications

(11 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the identified algorithms for these industry 4.0 technologies are the following: Breadth First Search (BFS) algorithm or Genetic Algorithms (GA) for DAS systems [ 53 , 55 ], Principal Component Analysis (PCA) algorithm for monitoring rail breakage [ 18 ], Artificial Bee Colony (ABC) algorithm for a train traction control systems [ 57 ], Dynamic Differential Evolution (RHMDE) algorithm for tracking the rail state [ 10 ], fuzzy systems or deep-learning models for rail maintenance [ 28 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

View full text Add to dashboard Cite

Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

show abstract

Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The proposed architecture investigated the characteristics of time-series signatures during a short period and classified the associated track segment to normal or defect states with an automated labelling method that used label defects for associated time-series signatures during the training phase. A convolutional neural network and extreme learning machine algorithm were used in studies [ 87 , 88 ] to detect different abnormalities in the axle-box measurements of the acceleration signal and their location using the global positioning system. The measurements were pre-processed before the model training by using a continuous wavelet transform.…”

Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

View full text Add to dashboard Cite

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

show abstract

“…Due to their high performance and promising results, convolutional neural has led to extreme weather, while demand causes the industry to raise capacity and increase the number of trains in the system. Nevertheless, learning machine algorithms can estimate the exact abnormalities by monitoring rail tracks [27] to perform risk assessment of rail failure [128], diagnose track circuit faults [129] provide early and precise detection methods that are essential for avoiding risks [130] and provide information for decision support [131]. It has been shown that video camera inspection is a flexible, effective and automatic method for monitoring rail tracks.…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

View full text Add to dashboard Cite

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

show abstract

Optimized railway track health monitoring system based on dynamic differential evolution algorithm

Cited by 30 publications

References 45 publications

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

A Deep Learning Approach Towards Railway Safety Risk Assessment

Contact Info

Product

Resources

About