ENSURING TRAIN SAFETY ON HIGH-SPEED RAIL-HIGHWAY

Modern high-speed trains in normal operation develop speeds of up to 350-400 km / h, and in tests they can even accelerate to 560-580 km/h. Due to the speed of service and high speed of movement, they seriously compete with other modes of transport, while maintaining such a property of all trains as low cost of transportation with a large volume of passenger traffic. For the first time the regular movement of high-speed trains began in 1964 in Japan under the Shinkansen project. In 1981, VSNT trains began to run in France, and soon most of Western Europe, including even the island of Great Britain, became connected by a single high-speed rail network. At the beginning of the XXI century, China became the world leader in the development of a network of high-speed lines, as well as the operator of the first regular high-speed maglev.In Russia, the regular operation of high-speed trains "Sapsan", on common tracks with conventional trains, began at the end of 2009. Since 2013, the idea of building the first specialized high-speed railway Moscow-Kazan (cargo-passenger) for the national high-speed traffic system has been discussed.Most of the high-speed trains carry passengers, but there are varieties designed for the transport of goods. For example, the French service La Poste has used special TGV electric trains for 30 years, which served to transport mail and parcels (their operation was completed in June 2015 due to the decrease in the volume of mailings in recent years).

A Hybrid Method to Obtain the Wheel–Rail Contact Point at Extreme Positions

When conducting a numerical simulation of a train’s derailment and post-derailment, it is necessary to continuously observe the relative position of the wheel and rail, which is of great significance for the correct evaluation of train safety. In this paper, a non-analytic method is proposed to extend the search range and improve the accuracy of the classical semi-analytical method, i.e. the contact locus method. Based on the point cloud convex hull, a high-density wheel contact locus vertical profile is obtained by projecting the chamfer and internal zone of the flange onto the rail cutting plane. To obtain maximum compression in the normal direction and avoid singularities on both rail head sides in the Cartesian coordinate system the rail surface is interpolated with the polar spline curve. Compared with the classical method used to describe the wheel contact locus, in the proposed hybrid method, potential contact points are provided. Finally, the proposed hybrid method and the classical methods are applied to the wheel track coupling simulation. Numerical results demonstrate the high reliability and effectiveness of the proposed method.

Auxiliary Stopping Area Layout Method for High-Speed Maglev Operated Bidirectionally on Single Track

Auxiliary stopping area (ASA) is the necessary emergency facility for train safety of the normal high-speed maglev. The study addresses the ASA layout problem of the high-speed maglev operated bidirectionally on single track. First, an optimization model of the ASA layout for unidirectional double-track lines considering train safety, operation efficiency, and construction cost is established, and two basic methods of the ASA layout are investigated based on the distance demand characteristic of ASAs. Then, the ASA layout problem of bidirectional single-track lines is analyzed, and an ASA two-way coordination layout algorithm (ASA-TWCLA) is proposed. Finally, a numerical experiment is carried out. The results suggest that under the premise of train safety and operation efficiency, compared with using the basic methods separately on the two directions, adopting the ASA-TWCLA algorithm can obtain a more economical ASA layout scheme for the same scenario.

A Convolutional Neural Network-Based Classification and Decision-Making Model for Visible Defect Identification of High-Speed Train Images

In high-speed train safety inspection, two changed images which are derived from corresponding parts of the same train and photographed at different times are needed to identify whether they are defects. The critical challenge of this change classification task is how to make a correct decision by using bitemporal images. In this paper, two convolutional neural networks are presented to perform this task. Distinct from traditional classification tasks which simply group each image into different categories, the two presented networks are capable of inherently detecting differences between two images and further identifying changes by using a pair of images. In doing so, even in the case that abnormal samples of specific components are unavailable in training, our networks remain capable to make inference as to whether they become abnormal using change information. This proposed method can be used for recognition or verification applications where decisions cannot be made with only one image (state). Equipped with deep learning, this method can address many challenging tasks of high-speed train safety inspection, in which conventional methods cannot work well. To further improve performance, a novel multishape training method is introduced. Extensive experiments demonstrate that the proposed methods perform well.

The problem of sand drifts on railways

The sand drift of the railway track and the blowing of the roadbed negatively affect the elements of the track infrastructure to such an extent that they reach the limit states at which it becomes impossible for them to continue functioning. This leads to a violation of train safety. To assess the negative impact of sand and determine the necessary protective measures, a method of visual monitoring of the state of the track is proposed.

The Fast and Continuous Railway Clearance Inspection Algorithm Based on Vehicle LiDAR Point Cloud

Railway clearance inspection is the foundation work to ensure train safety operation. Aiming at the problem of time-consuming and low accuracy of clearance inspection based on vehicle LiDAR point cloud, this paper proposes a fast and continuous railway clearance inspection algorithm. Firstly, the preliminary clearance inspection is completed by constructing the axial alignment bounding box of the clearance polyhedron. Then fine clearance inspection based on the topology relationship between point cloud and polyhedron to provide an accurate judgment. Finally, the clearance distribution information is obtained by Euclidean clustering. Based on three representative datasets, the proposed algorithm is validated, and the experimental results show that the accuracy and efficiency are enhanced by 25.0% and 83.4% responsibly.

Dynamic analysis of railway vehicle derailment mechanism in train-to-train collision accidents

Train collision-induced derailment may result in serious casualties. This study investigates the railway vehicle derailment behaviour and explores the derailment causes in train-to-train impacts. A train-track coupled dynamic model is developed and validated. The lateral ( Yde) and vertical ( Zde) relative displacements of the wheel/rail pair are used for the derailment evaluation. The results show that the wheelset jumping derailment and the saw-tooth buckling mode may occur during the high-speed collisions. Derailment is mainly caused by car body yawing rotation and wheelset lateral and vertical displacements. Yawing and pitching motions of car body influence wheelset displacement significantly. Vehicles with the higher velocity generate more severe derailment behaviour. Under circumstance of the same train impact energy, the higher vehicle mass and greater car numbers have the stabilizing influence for reducing the derailment risk. Increasing the force on the main energy-absorbing structure which is set in the front-end crushing zone of a vehicle to dissipate the train impact energy and the damping coefficient of the secondary yaw damper can decrease the derailment risk. The train safety operation areas and derailment boundaries indicate that the reliability of the Yde criterion is higher.