An automatic method for detecting sliding railway wheels and hot bearings using thermal imagery

Abstract

One of the most important safety-related tasks in the rail industry is an early detection of defective rolling stock components. Railway wheels and wheel bearings are the two components prone to damages due to their interactions with brakes and railway track, which makes them a high priority when the rail industry investigates improvements in the current detection processes. One of the specific wheel defects is a flat wheel, which is often caused by a sliding wheel during a heavy braking application. The main contribution of this paper is the development of a computer vision method for automatically detecting the sliding wheels from images taken by wayside thermal cameras. As a byproduct, the process will also include a method for detecting hot bearings from the same images. We first discuss our automatic detection and segmentation method, which identifies the wheel and bearing portion of the image. Then, we develop a method, using histogram of oriented gradients to extract the features of these regions. These feature descriptors are later employed by support vector machine to build a fast classifier with a good detection rate, which can detect abnormalities in the wheel. At the end, we train our algorithm using simulated images of sliding wheels and test it on several thermal images collected in a revenue service by the Union Pacific Railroad in North America.

Importance and Applications of Robotic and Autonomous Systems (RAS) in Railway Maintenance Sector: A Review

“…The proposed methodology consists of a wheel and bearing detection module, hot bearing detection module using thresholding, and a Support Vector Machine (SVM) classifier, which uses a Histogram Oriented Gradient (HOG) of thermal images as input to detect flat wheels. The developed system is capable of detecting simulated and real-world defective wheels, with 98% accuracy [39]. Furthermore, a study has been conducted to determine the feasibility of inspecting rail underframe components of freight cars, such as center sill, side-sills, and cross-bearers.…”

A method for wayside bogie monitoring based on internal yard running vibration

“…Wayside vehicle status monitoring devices have existed since the 1970s, and various types continue to be developed. 8–46 There is, however, no equipment available for conducting a non-dismantling functional inspection on the stiffness of critical bogie rubber components subjected to forces of several tens of newtons or more. Thus, we decided to study this method and to monitor the status of the bogie by using a wayside vehicle status monitoring device to measure the vibration of the bogie when it passes over iron plates (approximately 10 mm thick) placed on the track, or over the joints of the track.…”

Adaptive fault feature extraction from wayside acoustic signals from train bearings

“…These provide high quality information but require sensors and equipment to be fitted to every bearing on every vehicle. Acoustic emission [4] and hot axle box [5] detection systems can be fitted to one location in the network, but the former requires phyical access to the track, while the latter is only suitable for detecting late stage faults [6]. Wayside acoustic detection is another technology that is becoming increasingly popular because one monitoring station will observe multiple vehicles, no physical track access is required in order to install the equipment, and detection is at an earlier stage than its thermal counterpart.…”