Rail Inspection Meets Big Data: Methods and Trends

Li, Qingyong; Zhong, Zhangdui; Liang, Zhengping; Liang, Yangyang

doi:10.1109/nbis.2015.47

Cited by 38 publications

(15 citation statements)

References 17 publications

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“…The collection of rail inspection data has increased in volume and quality in recent years and has been presented as a big data analytics challenge [115]. Rail inspection processes have developed from two approaches: a) on-board sensors mounted on rolling stock and b) track-mounted sensors.…”

Section: A Consideration Of Big Data Analytics Applications For Railmentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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Nowadays, huge amounts of data have been captured along with the day-today operation of assets including railway systems. Hence, we have come to the era of big data. The utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. The vital information such as precursor information collected on failure modes and knowledge that may be available for analysis is hidden within the large extent of data. There are analysis tools incorporated with techniques such as multiple regression analysis and machine learning that are facilitated by the availability of big data. Therefore, the utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. This paper provides a review of the requirements and challenges for big data analytics applications to railway asset management. The review focuses on railway asset data collection, data management, data applications with the implementation of Blockchain technology as well as big data analytics technologies. The need for, and the importance of big data analytics in railway asset management; and the requirement for the asset condition data collection in the railway industry are highlighted. Research challenges in railway asset management via application of big data analytics are identified and the future research directions are presented. INDEX TERMS Big data analytics, asset management, railway, data management, blockchain.

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Section: A Consideration Of Big Data Analytics Applications For Railmentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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“…Finally, the F 1 value is given by Eq. (12). The software and hardware components used in the proposed system are given in Table 3.…”

Section: T P R = T P T P + F N F P R = F P F P + T N Accmentioning

confidence: 99%

“…This method, called contact-based, diagnoses faults by using graphs obtained from the friction between the mechanical device and the rail. It is fast and provides accurate results; however, because it requires contact with the rail, it may damage the rails or increase existing damage [12].…”

Section: Railway Analysismentioning

confidence: 99%

An adaptive fault diagnosis approach using pipeline implementation for railway inspection

Santur¹,

Karaköse²,

Akın³

2018

Turk J Elec Eng & Comp Sci

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Railway tracks must be periodically inspected. This study proposes a new approach for eliminating two major disadvantages experienced during rail inspection applications performed via computer vision. The first is the blurring effect on images, resulting from physical vibration during movement on the rail lines. This effect significantly reduces the high accuracy rate expected from anomaly inspection algorithms. The second disadvantage is the need to operate in real time. This study presents a new three-stage computer vision method approach that eliminates both disadvantages. First, a three-stage pipeline architecture is implemented and IMU-assisted blur detection is performed on images taken from the left and right rail lines. Next, a convolutional neural network is used for learning. In the third test stage, anomaly detection and classification training are conducted. By performing the implementation with parallel programming on graphic processing units, a highly accurate, cost-effective computer vision rail inspection, based on image processing and capable of operating in real time, is successfully carried out.

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“…The anomalies that could occur or are present on railway can lead to both troubles and financial losses and accidents in the transportation system. Therefore, the rail line should be checked periodically [3]. Today, the rail inspection operation is basically divided into two groups as contact and contactless methods.…”

Section: Introductionmentioning

confidence: 99%

“…The inspection operation consists of the steps of determining the components of the rail line and the deficiency (such as missing traverse and bolt) or anomaly conditions in these components [7][8][9][10][11][12][13]. The rail inspection applications in which light source and high resolution cameras are used may lead to the generation of false _______________________________________________________________________________________________________________________________________________________________ 1,2,3 positive results by detecting the stains formed by oil and dust particles on the rail line as anomaly in addition to high accuracy ratios and real-time operating speeds [8]. When three-dimensional laser cameras are compared with normal cameras, they involve both rgb data and precise distance information on the two-dimensional plane.…”

Section: Introductionmentioning

confidence: 99%

Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks

Santur¹,

Karaköse

Akın

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:Detecting the rail surface faults is one of the most important components of railway inspection process which should be performed periodically. Today, the railway inspection process is commonly performed using computer vision. Performing railway inspection based on image processing can lead to false-positive results. The fact that the oil and dust residues occurring on railway surfaces can be detected as an error by the image processing software can lead to loss of time and additional costs in the railway maintenance process. In this study, a hardware and software architecture are presented to perform railway surface inspection using 3D laser cameras. The use of 3D laser cameras in railway inspection process provides high accuracy rates in real time. The reading rate of laser cameras to read up to 25.000 profiles per second is another important advantage provided in real time railway inspection. Consequently, a computer vision-based approach in which 3D laser cameras that could allow for contactless and fast detection of the railway surface and lateral defects such as fracture, scouring and wear with high accuracy are used in the railway inspection process was proposed in the study.

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Rail Inspection Meets Big Data: Methods and Trends

Cited by 38 publications

References 17 publications

Requirements for Big Data Adoption for Railway Asset Management

Requirements for Big Data Adoption for Railway Asset Management

An adaptive fault diagnosis approach using pipeline implementation for railway inspection

Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks

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