The Rail Surface Defects Recognition via Operating Service Rail Vehicle Vibrations

• Published in: Machines (Basel) 2022-09, Vol.10 (9), p.796
• Main Authors: Zheng, Shubin, Zhong, Qianwen, Chen, Xieqi, Peng, Lele, Cui, Guiyan
• Format: Article
• Language: English
• Subjects: Axleboxes; Correlation coefficients; deep convolutional neural network (DCNN); Deep learning; Defects; Equipment costs; High speed rail; Image coding; intelligent algorithm; International economic relations; Kurtosis; Methods; Neural networks; Object recognition; Optimization; Physical work; rail surface defects recognition; Railroads; Rails; Random noise; Sensors; Signal processing; Surface defects; Training; variational mode decomposition (VMD); Vehicles; Vibration
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines10090796

Rail surface defects will not only bring wheel rail noise during train operation, but also cause corresponding accidents. Most of the existing detection methods are manual detection, which is time-consuming, laborious, inefficient, and subjective. With the development of technology, automatic detection replaces manual detection, which reduces manual labor, improves efficiency, and objectively evaluates the surface state of rails, which is in line with the purpose of modern intelligent production. The automatic detection of a single sensor is usually not enough to complete the recognition, but multiple sensors need to be additionally installed and refitted on the service vehicle, which creates difficulty for on-site test conditions. Therefore, in order to overcome these shortages and to adapt to the actual vibration characteristics of service vehicles, a rail surface defect recognition method based on optimized VMD gray image coding and DCNN is proposed in this paper. Firstly, the optimization method of VMD mode number based on the maximum envelope kurtosis is proposed. The VMD after parameter optimization is used to decompose the four-channel axle box vibration signal, and the component with the largest correlation coefficient between each order eigenmode component and the original signal is extracted. Secondly, the filtered IMF components are arranged in sequence and encoded into grayscale images. Finally, the DCNN structure is designed, and the training set is input into the network for training, and the test set verifies the effectiveness of the network and realizes the recognition of rail surface defects. The test accuracy of railway data set measured on the serviced vehicle is 99.75%, and the results show that this method can accurately identify the category of rail surface defects. After adding Gaussian noise to the original signal, the test accuracy reaches 99.20%, which proves that the method has good generalization ability and anti-noise performance. Additionally, this method can ensure the safe operation of vehicles without adding new equipment, which reduces operation costs and improves the intelligent operation and maintenance of rails.