Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches

Year 2016, Special Issue (2016), 307 - 313, 01.12.2016

Mehmet Karaköse , Orhan Yaman , Erhan Akın

https://doi.org/10.18100/ijamec.270627

Cited By: 4

Abstract

Today, railway
transportation is one of the transport modes commonly used. Compared to other
transport modes, railway traffic is highly critical. Multiple railway vehicles
run constantly on one or two lines. Rail switch passages are used to prevent
locomotives from colliding with one another and avoid traffic disruptions.
Through switch passages, locomotives pass from one line to another. Friction
between rail and wheels on switch passages is considerably high. This friction
leads to failures on switch passages. Unless these failures are diagnosed early
and remedied, significant accidents emerge.  

In this study, a new
approach based on image processing has been presented for detection of rail
switch passages on railway lines. A test vehicle has been created in order to
test the proposed approach and apply it on a real-time system. Railway line is
monitored by digital cameras fixed on this test vehicle. Image-processing
approach is developed on the real-time images captured from the railway line
and the switch passages on the line are detected. In addition, by specifying
the train route, the fault which occurring at the point of the switches is
detected. The image-processing approach consists of three main parts including
pre-processing, feature extraction and processing of the features obtained. At
the pre-processing stage, the basic image processing methods are used. At the
feature extraction stage, Canny edge extraction algorithm is used and hence the
edges in the image are detected. Hough transform method is used at the stage of
processing of the extracted features. Following Hough transform stage, straight
lines and angles of these lines are obtained on the image. Taking into account
the angle of each straight line, the junction points of the lines are
calculated. Thus, rail switch passage and switch types are detected. The
proposed image-processing approach is highly fast and real time-based. Compared
to the existing studies in the literature, it is seen that the proposed method
gives fast and successful results. This study intends to diagnose the failures
on switch passages early and prevent potential accidents.

Keywords

Railway, Condition Monitoring, Fault Detection, Image Processing, Railroad Switches

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