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Detection of Sliding Wheels and Hot Bearings Using Wayside Thermal Cameras

[+] Author Affiliations
Hanieh Deilamsalehy, Timothy C. Havens, Pasi Lautala

Michigan Technological University, Houghton, MI

Paper No. JRC2016-5711, pp. V001T02A002; 7 pages
  • 2016 Joint Rail Conference
  • 2016 Joint Rail Conference
  • Columbia, South Carolina, USA, April 12–15, 2016
  • Conference Sponsors: Rail Transportation Division
  • ISBN: 978-0-7918-4967-5
  • Copyright © 2016 by ASME


Train car wheels are subjected to different types of damages due to their interactions with the brake shoes and track. If not detected early, these defects can worsen, possibly causing damage to the bogie and rail. In the worst-case scenario, this rail damage can possibly lead to later derailments, a serious concern for the rail industry. Therefore, automatic inspection and detection of wheel defects are high priority research areas. An automatic detection system not only can prevent train and rail damage, but also can reduce operating costs as an alternative for tedious and expensive manned inspection. The main contribution of this paper is to develop a computer vision method for automatically detecting the defects of rail car wheels using a wayside thermal camera. We concentrate on identification of flat-spotted/sliding wheels, which is an important issue for both wheel and suspension hardware and also rail and track structure. Flat spots occur when a wheel locks up and slides while the vehicle is still moving. As a consequence, this process heats up local areas on the metal wheel, which can be observed and potentially detected in thermal imagery. Excessive heat buildup at the flat spot will eventually lead to additional wheel and possibly rail damage, reducing the life of other train wheels and suspension components, such as bearings. Furthermore, as a byproduct of our algorithm, we propose a method for detecting hot bearings. A major part of our proposed hot bearing detection algorithm is common with our sliding wheel detection algorithm. In this paper, we first propose an automatic detection and segmentation method that identifies the wheel and bearing portion of the image. We then develop a computer vision method, using Histogram of Oriented Gradients to extract features of these regions. These feature descriptors are input to a Support Vector Machine classifier, a fast classifier with a good detection rate, which can detect abnormalities in the wheel. We demonstrate our methods on several real data sets taken on a Union Pacific rail line, identifying sliding wheels and hot bearings in these images.

Copyright © 2016 by ASME
Topics: Bearings , Wheels



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