In the process of train operation, the contact friction between wheel and track will change the size of wheel set. In order to ensure the safe operation of train, it is of great significance to detect the change of wheel set size timely and accurately. On the basis of summarizing the research at home and abroad, the online dynamic detection technology of wheel set size is studied. Firstly, the overall architecture of the wheel set size online detection system is studied, the working principle of each detection module is explained, and the installation parameters of the laser displacement sensor are designed. Secondly, the machine learning algorithm of online dynamic detection technology of wheel set size is studied. The passing wheel sets are photographed by CCD camera, and then the three-dimensional model is reconstructed by point cloud data, and then the parameters of each part are measured and calculated according to the model. The wheel set size can be obtained.

Retraction: [Jun Mao, Jun Ma, Research on wavelet neural network PID control of maglev linear synchronous motor, IET Circuits, Devices & Systems 2022 (https://doi.org/10.1049/cds2.12136)]. The above article from IET Circuits, Devices & Systems, published online on 8 December 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

In order to improve the efficiency of production and commissioning of high-speed EMU and the ability of statistical analysis of production data.Based on Web mobile communication technology, taking the commissioning production line as the test carrier, the SSM framework is used to realize the design of digital commissioning and management platform, and create a set of commissioning scenarios applied to high-speed EMUs, which can realize the functions of mobile operation, digital commissioning and management, big data analysis and so on.Using the characteristics of automatic inspection, automatic judgment and data management of the platform, manual judgment can be reduced and operation efficiency can be improved.At the same time, real-time monitoring, quality control and intelligent adjustment of the whole commissioning process can be realized through the platform storage and management function.Through field application verification, the platform can effectively solve the waste of human and material resources in the commissioning process, At the same time, it improves the production efficiency and product quality, and provides a portable intelligent debugging and management scheme for high-speed EMU manufacturing and maintenance enterprises.

To overcome the sensitivity of voltage source inverters (VSIs) to parameter perturbations and their susceptibility to load variations, a fast terminal sliding mode control (FTSMC) method is proposed as the core and combined with an improved nonlinear extended state observer (NLESO) to resist aggregate system perturbations. Firstly, a mathematical model of the dynamics of a single-phase voltage type inverter is constructed using a state-space averaging approach. Secondly, an NLESO is designed to estimate the lumped uncertainty using the saturation properties of hyperbolic tangent functions. Finally, a sliding mode control method with a fast terminal attractor is proposed to improve the dynamic tracking of the system. It is shown that the NLESO guarantees convergence of the estimation error and effectively preserves the initial derivative peak. The FTSMC enables the output voltage with high tracking accuracy and low total harmonic distortion and enhances the anti-disturbance ability.

Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and mAP@0.5 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process.