Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.

Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization.

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

The train plug door is the only way for passengers getting on and off. Its failures will make train operation ineffective. Taking the developed digital signal processing technologies into consideration, a data-driven diagnosis method for train plug doors is proposed based on sound recognition. First, a novel preprocessing method based on empirical mode decomposition and hybrid intrinsic mode functions (IMFs) selection criterion is proposed. The selected significant IMFs are used to reconstruct the signals. Inspired by the idea of fractional calculus, novel entropy named fractional wavelet package decomposition energy entropy (FWPDE) is proposed. Finally, multi-class support vector machine is used for classification and validation. Experimental results indicate that the proposed preprocessing method is of great significance to extract effective FWPDE features. In addition, FWPDE is more powerful in comparison with the classical wavelet package decomposition energy entropy. The identification accuracy using the proposed method reaches 96.28%, which demonstrates its effectiveness and superiority.

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort.

Railway point machines (RPMs) is one of the most vital devices closely related to the efficiency and safety of train operation. Considering the advantages of contactlessness and easy-to-collect of sound signals, a novel sound-based fault diagnosis method for RPMs is proposed. First, fractional calculus is introduced to wavelet packet decomposition energy entropy (WPDE). Fractional WPDE (FWPDE) is then proposed, which is verified to be a more effective tool for fault feature representation. Second, coarse-grain process is firstly introduced to FWPDE. Novel feature named multi-scale FWPDE is developed, which can significantly improve fault diagnosis accuracy. Third, to select optimal feature set and optimize the hyperparameters of support vector machine (SVM) at the same time, a synchronous optimization strategy based on binary particle swarm optimization (BPSO) is presented, which can further improve the diagnosis accuracy. The superiority and effectiveness of the proposed method are verified by comparing to some existing fault diagnosis methods. The diagnosis accuracies of reverse-normal and normal-reverse switching processes reach 99.33% and 99.67%, respectively. Especially, the proposed method is suitable for diagnosis of similar faults, which can also provide reference for similar research fields.