ABSTRACT As a critical load‐bearing and running component of railway systems, the wheelset's operational safety fundamentally depends on precise detection and localisation of tread defects. Current deep learning‐based detection methods face significant challenges in extracting discriminative edge features under small‐sample conditions, leading to suboptimal defect localisation accuracy. To address these limitations, this study proposes TLEAR‐Net, a novel defect detection framework integrating transfer learning with an edge‐adaptive reinforcement attention mechanism. The methodology employs RetinaNet as the baseline architecture, enhanced through multi‐stage domain adaptation using COCO 2017 pretraining and parameter‐shared ResNet‐50 backbone optimisation to bridge cross‐domain feature discrepancies. An innovative edge‐adaptive reinforcement (EAR) attention module is developed to selectively amplify defect boundary features through learnable gradient operators and hybrid spatial‐channel attention mechanisms. Comprehensive evaluations on a proprietary data set annotated defect samples demonstrate the framework's superior performance, achieving state‐of‐the‐art detection accuracy (89.22% mAP) while maintaining real‐time processing capability (42.45 FPS).

To solve the problems such as poor anti-interference performance and more steady-state current harmonic components of a permanent magnet synchronous motor(PMSM) drive system based on traditional PI control, this paper presents a model predictive control method based on sliding-mode-controller (SMC-MPC). Firstly, a sliding mode speed controller based on exponential reaching law is designed to replace the traditional PI speed controller, and to reduce the influence of the load disturbance on system performance. Secondly, the model predictive current control is adopted based on the discrete model of PMSM to reduce current harmonics and improve the torque control accuracy. Then the cost function is designed by controlling the d- and q-axis current, and the optimal output voltage vector is obtained to drive the motor. Finally, compared with the traditional PI method, the simulation results show that the proposed method not only improves the robustness of the system to load disturbance effectively, but also decreases the ripples of current and torque. The presented method has good dynamic and static performance for PMSM drive system.

This paper investigates a synchronous control method with variable proportion for multi-axis servo system based on sliding mode variable structure to solve the inherent shortcomings of parallel synchronous uncoupled control for multi-servo motors. The method is an improvement of the ring-coupled control strategy and is thus applicable to multi-axis proportional synchronous control. No-salient pole permanent magnet synchronous servo motors are taken as the controlled objects. A sliding mode controller is designed according to the state equation of the mathematical model. The rationality of the designed controller is proven by Lyapunov stability theory, and the method is compared with a parallel synchronous control strategy, which is widely used in the object of this research. Finally, the feasibility and superiority of the method in a multi-axis synchronous control system with variable proportion is confirmed by MATLAB simulation and experimental verification. The method is applied to a three-degree-of-freedom servo system, and makes the theoretical research have the corresponding practical engineering application value.

To improve the torque precision of permanent magnet synchronous motor (PMSM) in railway vehicles, a nonsingular terminal sliding mode observer (NTSMO) is designed to detect permanent-magnet (PM) demagnetization, and a precise torque control method is proposed for the railway PMSM vector control system. First-of-all, the PMSM mathematical model of PM demagnetization is presented. Secondly, a NTSMO is constructed to reconstruction PM flux linkage in d-q reference frame. Meanwhile, the observer stability proof is given. The estimated torque could be calculated according to the estimated PM flux linkage. Finally, the overall system is verified by simulation in Matlab/Simulink. Compared with the sliding mode observer method, the torque control precision of the driving system could be improved, and the driving performance of railway vehicles is fulfilled and guaranteed.

Vector quantization encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the encoding procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.

This paper is concerned with the problem of stability of neural networks with time-varying delays. A novel Lyapunov-Krasovskii functional decomposing the delays in all integral terms is proposed. By exploiting all possible information and considering independent upper bounds of the delay derivative in various delay intervals, some new generalized delay-dependent stability criteria are established, which are different from the existing ones and improve upon previous results. Numerical examples are finally given to demonstrate the effectiveness and the merits of the proposed method.

A design method of an intelligent humidity controller based on a field-bus is proposed, which is aimed at the reformation of an automatic plumbum sintering producing system. The system process is first analyzed. Then the system is constituted with foundation field-bus devices. Finally, the humidity control algorithm is given, which is based on an intelligent variable structure controlled with sliding mode. The practical application shows that this design can effectively control time varying systems with long time lag, such as the mixture humidity.