Train-based differential eddy current sensor system for rail fastener detection

Document identifier: oai:DiVA.org:ltu-76340
Access full text here:10.1088/1361-6501/ab2b24
Keyword: Engineering and Technology, Civil Engineering, Other Civil Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Annan samhällsbyggnadsteknik, Fastener, Clamps, Differential eddy current sensor, Detection, Inspection, Drift och underhållsteknik, Operation and Maintenance
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communities
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Praneeth Chandran

Luleå tekniska universitet; Drift, underhåll och akustik
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Matti Rantatalo

Luleå tekniska universitet; Drift, underhåll och akustik
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Johan Odelius

Luleå tekniska universitet; Drift, underhåll och akustik
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Håkan Lind

Bombardier Transportation, Stockholm, Sweden
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Stephen M. Famurewa

Luleå tekniska universitet; Drift, underhåll och akustik
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