Jidong Lv

Electronic equipment is essential to railway transport, as its performance directly impacts the safety of train operations. However, current operation and maintenance practices face several problems, including unrecognized degradation of equipment performance, difficulty in diagnosing faults after they occur, and coarse-grained maintenance strategies, These problems may lead to an excessively broad scope of fault impact, the continued operation of defective equipment, and other serious consequences. In this context, the paper presents an intermittent fault (IF)-based health management approach for railway electronic equipment. Building on fault mechanism analysis for electronic equipment, a mathematical parameterized characterization of intermittent faults is achieved by integrating the fault frequency and intensity vectors into the state transition probability matrix of a hidden Semi-Markov model (HSMM). To solve the non-convex parameter estimation problem, a global optimization algorithm based on swarm intelligence is constructed to maximize the log-likelihood objective function, so as to evaluate the current operational state of the equipment. The paper then uses the equipment observation sequences predicted by the long short-term memory network (LSTM) to obtain the remaining useful life (RUL) values at any timepoint. The estimation serves as the basis for proactively adjusting maintenance decisions. In experiments based on real-world power module faults, compared with the conventional Baum–Welch trained HSMM method, the proposed method in this paper improves state recognition accuracy by 2.15% and reduces the RMSE of RUL prediction by 16.67%, verifying its effectiveness.

Grouping train control for high‐speed railways is an advanced control scheme. It allows the following trains to maintain a close headway based on the real‐time and accurate trajectory of the preceding train. However, due to nonlinear and uncertain dynamics as well as complex operating conditions, the trajectory of the preceding train is difficult to directly obtain or accurately predict over a long horizon. This paper proposes a personalized trajectory prediction framework that integrates Gaussian mixture model GMM‐based driving style recognition and long short‐term memory (LSTM) neural networks. It accounts for the individual driving characteristics of the preceding train, enabling accurate long‐term trajectory prediction under nonlinear dynamics. For the following train, a model predictive control (MPC)‐based close‐tracking scheme is designed that incorporates the predicted trajectory to extend the prediction horizon of the controller, while a safety margin is introduced to tighten safety constraints. Experimental results show that our approach maintains a 15‐s mean relative error (MRE) below 3.5%, achieving improvements of 32.9% and 43.3% over standard LSTM and Kalman filter (KF) approaches, respectively. Compared with a conventional MPC, our approach substantially reduces the required following distance while maintaining control stability and operational safety, shortening the tracking interval to 1520 m, particularly at high‐speed operating levels.

This study addresses the issue of communication link interruptions in train platoon control under the complex operating environment of high-speed railways. An adaptive trajectory tracking control approach based on a finite-time sliding mode is proposed under jointly connected switching topologies. The proposed framework ensures platoon consensus under jointly connected switching topologies, where the leader’s information need not reach all followers in each topology but only collectively over a finite interval, thus relaxing connectivity requirements and enhancing practicality. Within this framework, the sliding surface is designed to incorporate relative error signals determined by the communication topology, and an adaptive mechanism is employed to effectively handle unknown external disturbances without requiring prior knowledge of their bounds or derivatives. Simulation results demonstrate that, compared with MPC and non-adaptive approaches, the proposed strategy reduces position errors by approximately 80% and velocity errors by around 40%, significantly improving platoon tracking accuracy. Furthermore, simulations under both periodic and random topology switching indicate that periodic switching achieves performance closer to that of a fully connected topology, further enhancing trajectory tracking effectiveness.

Train platoons are a really innovative application for future railways, in which trains are virtually-coupled via Train-Train communication, so drastically reducing their headways and increasing line capacity. However, as the spacing between trains becomes closer, the influence of disturbances from preceding trains becomes more significant and cannot be ignored. This influence can have a substantial impact on the operational behavior of the following trains, leading to fluctuating spacing within the platoon and potentially compromising the safety and stability of the train spacing. This paper introduces a formalized modeling framework and a controller synthesis method for train platoons, aiming to achieve absolute safety and stable operation in train platoon operations. The framework utilizes Hybrid Priced Two-player Timed Game Automaton (HPTTGA) to describe train platoon dynamics and synthesizes controllers that satisfy safety requirements using an On-the-fly Controller Synthesis (OCS) algorithm, where the effectiveness of the algorithm is demonstrated through a two-train tracking example, and Q-learning is applied to select controller which satisfy local and string stability. Owing to model-based feature, the proposed method is compared to classical Model Predictive Control (MPC) in terms of safety, local stability, and string stability. Experimental results demonstrate that the proposed method consistently ensures train platoon safety and achieves similar control performance to MPC in extreme operating scenarios. Moreover, under velocity limit scenarios, the proposed method successfully achieves the desired following behavior of the trailing train according to the control objectives.

Train platoon improves railway efficiency by coordinating train speeds and inter‐train distances. However, actuator delays pose a major challenge to maintaining safe dynamic spacing. This study develops a safety‐oriented framework that integrates hybrid control with reachable set estimation to explicitly compute the minimum safe separation under actuator delays. A unified modelling strategy is proposed that incorporates actuator delays together with real‐time acceleration disturbances of the leading train. By using forward reachable set analysis, the effect of actuator delays on position errors is estimated and incorporated into the controller to compensate for such delays, thereby improving the safety and robustness of train platoon tracking. The influence of actuator delays on safety during emergency braking scenarios is evaluated through simulation and field experiments. The results show that the forward reachable set of position errors expands as the actuator delays increase. The adoption of controller can reduce the influence of actuator delay on the safety margin by approximately 60%. Compared with the method of eliminating delay using the Lyapunov–Krasovskii functional method, the proposed method ensures the safety of the tracking distance of the train platoon.

Trajectory prediction of the train ahead is vital for deciding the minimum safe distance for train separation. An important research challenge, in this regard, is how to effectively and accurately estimate the trajectory of the train ahead using spatiotemporal approaches with train operating data. In this paper, we present a novel hybrid model for predicting train trajectories by integrating Long Short-Term Memory (LSTM) and Kalman Filter (KF). In the proposed hybrid LSTM-KF model, the LSTM model is used to analyze time series data and discover the long-term dependencies of train trajectory data, which can be assumed as observation data for the KF model. The KF model can combine train dynamics mechanism to extract local features of train operation data, so as to smooth the train trajectory predicted by LSTM model. A novel on-the-fly algorithm is devised for efficient realization of the proposed LSTM-KF model. Experiments are carried out using the train data collected from Chengdu Metro Line 8. The results indicate that our hybrid LSTM-KF model has greatly improved the accuracy of long-term train trajectory prediction.