Novel Multistate Fault Diagnosis and Location Method for Key Components of High-Speed Trains

Abstract

Images collected by linear scan cameras are stretched and compressed due to the speed regulation of trains. This condition changes the shape of objects and considerably increases the false and missed alarm rates. Moreover, small size and dense distribution of the key components in railway trains increase the difficulty in fault diagnosis. Therefore, a two-module troubleshooting and positioning methodology is proposed, in this article, for state diagnosis of key small components of high-speed running trains. First, the image with deformation is reshaped to be exactly the same as the standard one via omnidirectional scale correlation normalization (OSCN), which performs well even in low texture, high-light situations. Second, we propose three feature-enhanced models to expand the receptive field of deep feature maps. This novel detector, namely, refine-inception net (RIN), not only reduces the rate of missed and false detection, but also minimizes the influence of target size and occlusion. Augmentation is performed to increase robustness because the detector is data driven. Experimental results show that the optimal strategy combining OSCN and RIN can troubleshoot high-speed trains of different models with an accuracy higher than 99%. Our method can be extended to foreign object recognition on the train roof while maintaining railway safety.