Autonomous switching of electric locomotives in neutral sections

Abstract

Abstract

Electrical locomotives traversing in a neutral section must switch off as they enter a different phase voltage. The current system used to auto-switch these electric locomotives requires two pairs of induction magnets installed adjacent in-between the rails and two sensors installed underneath the locomotives. However, the return cost of investment is low, maintenance costs increase due to failures, and locomotives do not auto-switch due to the degradation of magnet strength. Additionally, damage to sensors due to animal collisions or objects also causes switching failures, and vandalism and theft are some of the challenges limiting this switching scheme. Furthermore, the latter switching method does not align with the Transnet 4.0 strategy aimed at adopting the Fourth Industrial Revolution (41R). Therefore, to align with the Fourth Industrial Revolution, this research proposed a computer vision-based approach to switch electric locomotives automatically. The requirements are a computer, a high-definition camera, and open and close markers. While the latter gives an overview of the hardware used, creating a new dataset with training and testing images allowed for developing a machine learning classification model. Firstly, image pre-processing converts the RGB images to greyscale then the noise is removed using a bilateral filter. Secondly, segmentation and marker extraction is performed by employing the Sobel operator and Circular Hough Transform. Thirdly, features are extracted using a Histogram of Oriented Gradients and employing Linear Support Vector Machine to perform classification. However, before selecting the latter classifier, the feature extractor is tested against Quadratic Support Vector Machine, K-Nearest Neighbour and Convolutional Neural Network. The model's accuracy is then measured using the training set and ground truth dataset. The test set is used to validate the model with evaluation methods such as a confusion matrix, Fl-measure and 2-fold cross­ validation.