LAUSR

Bolt-loosening can cause poor running quality of trains, even resulting in terrible accidents. Currently, existing bolt-loosening detection methods for running trains need 3D data of the whole train body, extremely decreasing the efficiency of fault detection. In this paper, we propose a fast bolt-loosening detection method for the running train’s key components based on binocular vision. Since a train generally consists of many cars with the same structure, the position distribution of train’s key components is regular and periodic. First, we propose a novel method to detect key component regions including bolts, taking full advantage of this periodic distribution rule. Second, the sub-pixel edges of the bolt cap and mounting surface in the localized regions are extracted and segmented, respectively, combining with the convolutional neural network (CNN). Finally, based on stereo matching and the binocular vision model, the 3D data of these edges are obtained to calculate the distance between the bolt cap and mounting surface. By comparing the calculated distance with the reference value, we can judge whether bolt-loosening has occurred. The experimental results indicate that multi-bolt looseness can be calculated simultaneously. The measurement repeatability and precision are superior to 0.03 and 0.08 mm, respectively, and the relative error is less than 1.42%.