Railway Track Vibration Analysis and Intelligent Recognition of Fastener Defects

Abstract

The rail fastener is an indispensable component used to connect the rail and sleepers in the track structure. Real-time recognition of the fastener defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the fastener defects including the invisible defects appearing as bolt loosening and the visible defects such as the worn or completely missing fasteners by using axle-box vibration acceleration and deep learning network. First, the dynamical relation between the fastener defects and the axle-box vibration acceleration is investigated by using the first principle and the vehicle–track dynamical model. Then a defects recognition network is built based on the deep convolution neural network for track fasteners by using the frequency spectrum images of the axle-box vibration. The results show that the proposed method achieves a classification accuracy of 98.27%. Finally, the track section where the fasteners are most likely to be damaged is investigated, and rail corrugation is found to be a key factor that causes fastener fatigue.