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Train-based differential eddy current sensor system for rail fastener detection
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-2300-9716
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-8471-4494
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-0216-5058
Bombardier Transportation, Stockholm, Sweden.
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2019 (English)In: Measurement science and technology, ISSN 0957-0233, E-ISSN 1361-6501, Vol. 30, no 12, article id 125105Article in journal (Refereed) Published
Abstract [en]

One of the crucial components in rail tracks is the rail fastening system, which acts as a means of fixing rails to the sleepers to maintain the track gauge and stability. Manual inspection and 2D visual inspection of fastening systems have predominated over the past two decades. However, both methods have drawbacks when visibility is obscured and are found to be relatively expensive in terms of cost and track possession. The present article presents the concept of a train-based differential eddy current (EC) sensor system for fastener detection. The sensor uses the principle of electromagnetic induction, where an alternating-current-carrying coil is used to create an EC on the rail and other electrically conductive material in the vicinity and a pick-up coil is used to measure the returning field. This paper gives an insight into the theoretical background and application of the proposed differential EC sensor system for the condition monitoring system of rail fasteners and shows experimental results from both laboratory and field measurements. The field measurements were carried out along a heavy-haul railway line in the north of Sweden. Results obtained from both the field measurements and from the lab tests reveal that that the proposed method was able to detect an individual fastening system from a height of 65 mm above the rail. Furthermore, missing clamps within a fastening system are detected by analysing a time domain feature of the measurement signal.

Place, publisher, year, edition, pages
Institute of Physics (IOP), 2019. Vol. 30, no 12, article id 125105
Keywords [en]
fastener, clamps, differential eddy current sensor, detection, inspection
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-76340DOI: 10.1088/1361-6501/ab2b24ISI: 000487122500002Scopus ID: 2-s2.0-85075694567OAI: oai:DiVA.org:ltu-76340DiVA, id: diva2:1359786
Note

Validerad;2019;Nivå 2;2019-10-10 (johcin)

Available from: 2019-10-10 Created: 2019-10-10 Last updated: 2023-09-05Bibliographically approved
In thesis
1. Train Based Automated Inspection for Railway Fastening System
Open this publication in new window or tab >>Train Based Automated Inspection for Railway Fastening System
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Rail transportation is a sustainable mode of transportation and is a key enabler of the socio-economic development of modern society through passenger and freight services. Growth in overall transport demand has led to railways experiencing higher demand on operational capacity, service quality, and safety. However, an increase in traffic and load can lead to an increase in degradation of the components and thus cause a reduction in the infrastructure quality. Such degradation leads to failures of components, consequently resulting in a higher frequency of interventions for maintenance and renewal activities. The downtime arising from such maintenance and renewal of networks is a significant contributor to the delays incurred to the passengers. A plausible solution to attain higher operational capacity and quality of service with the existing infrastructure and minimise delays due to failure would be to inspect the track and its components frequently using in-service trains, operating in regular traffic. 

One of the crucial components in rail tracks is the rail fastening system, which acts as a means to fix the rails onto the sleeper, upholding the track stability and track gauge.  Failures of fasteners can increase wheel flange wear, reduce the safety of train operations, and may lead to derailment due to gage widening or wheel climb. In Sweden, the inspection of track fasteners is mainly carried out either manually by trained inspectors or by using measurement cars. Manual inspections are slow, cost-intensive, labour-intensive, pose safety issues for maintenance personal involved, and are prone to human errors. Inspections based on measurement cars are cost intensive and requires track possession and thus cannot be utilised frequently without compromising the operational capacity. Further, the adverse weather condition, especially in the north of Sweden for the majority of the year, limit regular fastener inspection that depends on such traditional inspection methods. The research presented in this thesis has aimed to find an automated method for fastener inspection that can be carried out using vehicle-mounted measuring equipment operating in regular traffic. 

Firstly, a study was carried out to determine the effectiveness of automated visual-based solutions for fastener state detection. An anomaly detection model combining image processing techniques and deep learning algorithms was developed to detect the fastener state from rail images captured during the vision-based inspection. The model had a high capability of detecting the fastener state from the rail images. However, the model had difficulties detecting the fastener when there were instances of occlusions of fasteners due to the presence of snow and ballast stones and when the image brightness was low. In Sweden, specifically the northern part of it, the fastening systems are covered under snow for up to six months and thus can inhibit regular fastener inspections that rely on such automated visual inspection methods. 

To overcome the challenges associated with automated visual inspection systems for fastener state detection, an alternative inspection method using a differential eddy current measurement system was investigated. Controlled field measurements were carried out along a heavy haul railway line in the north of Sweden to determine the effectiveness of the proposed measurement system. An anomaly detection model based on a supervised machine learning algorithm was developed to detect the fastener state from the controlled eddy current measurements. Further, to test the effectiveness of the eddy current sensor during real-time measurements, the proposed sensor system was mounted on an in-service freight train, and measurements were carried out along the iron ore line of Sweden. An anomaly detection model using unsupervised machine learning algorithms was developed to facilitate fastener state detection and detect other anomalies from the real-time measurement data.

Place, publisher, year, edition, pages
Luleå University of Technology, 2022
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Rail fastening system, clamps, visual inspection, differential eddy current sensor, machine learning, anomaly detection
National Category
Infrastructure Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-89090 (URN)978-91-8048-022-2 (ISBN)978-91-8048-023-9 (ISBN)
Public defence
2022-04-07, C305, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
Luleå Railway Research Centre (JVTC)
Available from: 2022-02-02 Created: 2022-02-02 Last updated: 2023-09-05Bibliographically approved

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Chandran, PraneethRantatalo, MattiOdelius, JohanFamurewa, Stephen M.

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