As a result of long‐term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R‐CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang‐Taiyuan high‐speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

The softmax function is widely used in deep neural networks (DNNs), its hardware performance plays an important role in the training and inference of DNN accelerators. However, due to the complexity of the traditional softmax, the existing hardware architectures are resource-consuming or have low precision. In order to address the challenges, we study a base-2 softmax function in terms of its suitability for neural network training and efficient hardware implementation. Compared to the classical base- $e$ softmax function, the base-2 softmax function is a new softmax function that uses 2 as the exponential base instead of $e$ . From the aspects of mathematical derivation and software simulation, we first demonstrate the feasibility and good accuracy of the base-2 softmax function in the application of neural network training. Then, we use the symmetric-mapping lookup table (SM-LUT) method to design a low-complexity architecture but with high precision to implement it. Under TSMC 28nm CMOS technology, an example design of our architecture has the area of $5676 ~\mu m^{2}$ and the power consumption of 13.12 mW for circuit synthesis at the frequency of 3 GHz. Compared with the latest works, our architecture achieves the best performance and efficiency.

The key to ensuring rotating machinery’s safe and reliable operation is efficient and accurate faults diagnosis. Intelligent fault diagnosis technology based on deep learning (DL) has gained increasing attention. A critical challenge is how to embed the characteristics of time series into DL to obtain stable features that correlate with equipment conditions. This study proposes a lightweight rolling bearing fault diagnosis method based on Gramian angular field (GAF) and coordinated attention (CA) to improve rolling bearing recognition performance and diagnosis efficiency. Firstly, the time domain signal is encoded into GAF images after downsampling and segmentation. This method retains the temporal relation of the time series and provides valuable features for DL. Secondly, a lightweight convolution neural network (CNN) model is constructed through depthwise separable convolution, inverse residual block, and linear bottleneck layer to learn advanced features. After that, CA is employed to capture the long-range dependencies and identify the precise position information of the GAF images with nearly no additional computational overhead. The proposed method is tested and evaluated by CWRU bearing dataset and experimental dataset. The results demonstrate that the CNN based on GAF and CA (GAF-CA-CNN) model can effectively reduce the calculation overhead of the model and achieve high diagnostic accuracy.

The track line suffers from track irregularity, subgrade settlement, rail surface defects and gauge distortion. Measuring the track geometry quality plays an important role for rail administrations to safe operation and track maintenance. In order to meet the requirements of track daily inspection, this paper describes a comprehensive detection system for the track geometry, which is designed by four cameras, an inclinometer and inertial measurement unit. Moreover, the detection system has the characteristics of simple structure, automatic detection and multiple measurement parameters. Firstly, to measure track alignment and irregularity, a multi-sensing calibration method is established to fuse visual sensors and inertial sensors. An accelerometer and a Kalman filter are used to eliminate accumulated unrealistic drifts of accelerometer and gyroscope. Secondly, to measure the rail surface defects and gauge, the model of laser and machine vision is deduced to obtain coordinate values in a unified coordinate system and an improved iterative closest point method is developed to improve the matching accuracy. Finally, a series of experiments are carried out to validate the measuring performance. The results indicate that the proposed method can accurately measure the track geometry, with the maximum deviations of track alignment, track irregularity, rail defects and gauge are less than 0.5mm, 0.4mm, 0.3mm and 0.1mm, respectively. The detection system is not only simple in structure but also has multiple detection parameters, which is especially suitable for track daily maintenance.

Softmax is a common and complex activation function in Deep Neural Networks (DNN). However, it is a challenge to apply it efficiently in DNN training hardware accelerator. Therefore, we propose a high precision calculation method and architecture based on base- $2$ softmax, which has low hardware complexity than base- $e$ softmax but can still be useful in DNN training. First, we simplify the hardware implementation complexity of calculating base- $2$ softmax. Second, we use the base-TEXPRESERVE4 hyperbolic COordinate Rotation Digital Computer (CORDIC) to implement the core computation. Finally, we show that the proposed method can be used in DNN training through experiments. Moreover, with the same order of the magnitude of high precision, our hardware cost is lower than traditional base- $e$ softmax or other alternative design methods. Under TSMC 28nm CMOS technology, an example design of our architecture has the area of $98787.43\mu m^2$ and the power consumption of $24.72$ mW for circuit synthesis at the frequency of 1GHz.

This paper aims to create a prediction model for car body vibration acceleration that is reliable, effective, and close to real-world conditions. Therefore, a huge amount of data on railway parameters were collected by multiple sensors, and different correlation coefficients were selected to screen out the parameters closely correlated to car body vibration acceleration. Taking the selected parameters and previous car body vibration acceleration as the inputs, a prediction model for car body vibration acceleration was established based on several training algorithms and neural network structures. Then, the model was successfully applied to predict the car body vibration acceleration of test datasets on different segments of the same railway. The results show that the proposed method overcomes the complexity and uncertainty of the multiparameter coupling analysis in traditional theoretical models. The research findings boast a great potential for application.