Abstract
The development of algorithms for detecting failures in railway catenary support components has, among others, one major challenge: data about healthy components are much more abundant than data about defective components. In this paper, virtual reality technology is employed to control the learning environment of convolutional neural networks (CNNs) for the automatic multicamera-based monitoring of catenary support components. First, 3D image data based on drawings and real-life video images are developed. Then, a virtual reality environment for monitoring the catenary support system is created, emulating real-life conditions such as measurement noise and a multicamera train simulation to resemble state-of-the-art monitoring systems. Then, CNNs are used to extract and fuse the features of multicamera images. Experiments are conducted for monitoring the cantilever support connection, both down (CSC-D) and up (CSC-U), and registration arm support connection, both down (RASC-D) and up (RASC-U). Experimental results show that the CNNs trained in the virtual reality environment can capture the most relevant spatial information of the catenary support components. Multicamera image detection based on CNNs detects screw loss for all four components. For CSC-D and RASC-U, normal and pin-loss images are also fully detected. A challenge remains in increasing the pin-loss detection for both CSC-U and RASC-D.
Original language | English |
---|---|
Title of host publication | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 2183-2188 |
Number of pages | 6 |
ISBN (Electronic) | 9781538670248 |
DOIs | |
Publication status | Published - 2019 |
Event | 22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019 https://www.itsc2019.org/ |
Conference
Conference | 22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019 |
---|---|
Country/Territory | New Zealand |
City | Auckland |
Period | 27/10/19 → 30/10/19 |
Internet address |