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.

This paper proposes a novel method of structural system modal identification, where the iterative method is introduced in symplectic geometric model decomposition (SGMD). The proposed method can decompose the measured response into finite symplectic geometric components and identify the modal parameters of time‐invariant structures and the instantaneous frequency of time‐variant system through each symplectic geometric component. To obtain the shape information of the structural model, the SGCs of the same frequency at different measuring points are subjected to singular value decomposition (SVD). Both simulated data verification and measured data verification were used to verify if the method proposed in this article is effective for time‐invariant system and time‐variant system identification. For the simulated data, we study on a structural system which is set up with time‐variant stiffness and time‐invariant system. The measured vibration data of beam structure and time‐variant wheel‐rail coupling system were also tested and varied. Compared with the results of empirical model decomposition, the proposed method is capable of identifying instantaneous frequencies with better accuracy.

At present, the methods widely used to evaluate passenger comfort in train environment are ISO2631-1 and UTC-513R. In order to compare and analyze the application of the two standards in the actual line conditions, three-axis acceleration sensor is installed on CRH3 to collect the vibration data of the vehicle. All the ride comfort is calculated according to the standards of ISO2631-1 and UIC-513R, and mapped to the map line. The results are helpful to further improve the accuracy of comfort evaluation of high-speed trains.

Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. The experimental results indicate that the proposed method achieves a maximum estimation error of only 10% within a depth range of 0.35 mm., when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.