Deep Learning based Virtual Point Tracking for Real-Time Target-less Dynamic Displacement Measurement in Railway Applications

In the application of computer-vision based displacement measurement, an optical target is usually required to prove the reference. In the case that the optical target cannot be attached to the measuring objective, edge detection, feature matching and template matching are the most common approaches in target-less photogrammetry. However, their performance significantly relies on parameter settings. This becomes problematic in dynamic scenes where complicated background texture exists and varies over time. To tackle this issue, we propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge. Our approach consists of three steps: 1) automatic calibration for detection of region of interest; 2) virtual point detection for each video frame using deep convolutional neural network; 3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail is measured during operation. We also implement an algorithm using template matching and line detection as the baseline for comparison. The numerical experiments have been performed to evaluate the performance and the latency of our approach in the harsh railway environment with noisy and varying backgrounds

Small-amplitude hunting diagnosis method for high-speed trains based on the bogie frame’s lateral–longitudinal–vertical data fusion, independent mode function reconstruction and linear local tangent space alignment

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.Hunting stability is an important factor that influences the running safety of high-speed trains. Most of the existing hunting monitoring methods monitor only the standard hunting. The small-amplitude hunting, however, not only affects ride comfort but also aggravates the wheel–rail wear. Therefore, to extend the service life of wheels and rails and to improve the ride comfort, it is extremely important to monitor the small-amplitude hunting. Hunting motion is a coupled movement of lateral and yaw displacements of the wheelset. When the bogie is in an unstable state, instability will occur not only in the lateral side but also in the longitudinal and vertical sides of the bogie. To improve the robustness of the small-amplitude hunting monitoring methods, this study proposes an idea of the bogie frame’s lateral–longitudinal–vertical data fusion. In addition, the small-amplitude hunting signals have strong nonlinear characteristics, and their frequency and amplitude are unstable. Using only the amplitude or frequency to detect the small-amplitude hunting has obvious shortcomings. Therefore, a new feature extraction method based on the independent mode function reconstruction and linear local tangent space alignment (IMFR-LLTSA) is proposed. This method has been tested with three simulated signals. Finally, a method of combining the bogie frame’s lateral–longitudinal–vertical data fusion and IMFR-LLTSA is used to identify the small-amplitude hunting of high-speed trains. This method has been validated using the data of the CRH380a high-speed train running on the Shanghai–Hangzhou line, monitored by the authors’ research group. The results show that this method is superior to the single lateral diagnosis method

Reducing wheel wear from the perspective of rail track layout optimization

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Wheel wear (W-wear) is one of the most critical issues affecting vehicle-track performances and operating costs. Currently, the works on W-wear behavior and W-wear reduction are mainly based on four aspects: wheel-rail (WR) tribology, WR profile, vehicle structure design and active control of vehicle suspensions. Little attention has been paid to the effects of track layout parameters, such as superelevation, gauge, and cant. To supplement the existing research, this work aims to investigate the relationship between W-wear and track layout parameters and ultimately reduce W-wear through optimizing track layout parameters. The framework consists of a series of steps. Firstly, a multibody dynamics simulation (MBS) model of an Sgnss wagon with 55 degrees of freedom (DOFs) is built. Then, taking a 375-m-radius curve as a case, the influence of track layout parameter (superelevation, gauge, and cant) on W-wear and vehicle derailment safety is investigated based on Kriging surrogate model (KSM). Finally, based on optimized results obtained by KSM and particle swarm optimization (PSO), two optimal regions and three reasonable suggestions concerning the layout of a 375-m-radius curve are given from the perspective of reducing W-wear. This study is promising for the parameter setting of those dedicated lines, on which the train speed is usually fixed, such as metro, light rail, and tram.EC/FP7/234079/EU/Railway Vehicle Dynamics and Track Interactions Total Regulatory Acceptance for the Interoperable Network/DYNOTRAI

Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line

Effect of mass distribution on curving performance for a loaded wagon

The location of wagon gravity center for a loaded wagon is underestimated in a vehicle–track coupled system. The asymmetric wheel load distribution due to loading offset significantly affects the wheel-rail contact state and seriously deteriorates the curving performance in conjunction with the height of gravity center and cant deficiency. Optimizing the location of gravity center and cruising velocity, therefore, is of interest to prevent the derailment and promote the transport capacity of railway wagons. This study aims to reveal the three-dimensional influencing mechanism of mass distribution on vehicle curving performance under different velocities. The wheel unloading ratio is regarded as the evaluation index. A simplified quasi-static model is established considering essential assumptions to highlight the influence of lateral and vertical offset on curving performance. For a more accurate description, the MBS models with various locations of wagon gravity center are built and then negotiate curves in different simulation cases. The simulation results reveal that the distribution of wheel unloading ratio determined by loading offset is like contour lines of ‘basin’. Based on the conclusions of quasi-static analysis and dynamics simulations, the regression equation is proposed and the fitting parameters are calculated for each simulation case. This paper demonstrates the necessity of optimizing the location of wagon gravity center according to the running condition and offers a novel strategy to load and transport the cargo by railway wagons.TU Berlin, Open-Access-Mittel – 202

Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study

The selection of a wheel profile is a topic of great interest as it can affect running performances and wheel wear, which needs to be determined based on the actual operational line. Most existing studies, however, aim to improve running performances or reduce contact forces/wear/rolling contact fatigue (RCF) on curves with ideal radii, with little attention to the track layout parameters, including curves, superelevation, gauge, and cant, etc. In contrast, with the expansion of urbanization, as well as some unique geographic or economic reasons, more and more railway vehicles shuttle on fixed lines. For these vehicles, the traditional wheel profile designing method may not be the optimal choice. In this sense, this paper presents a novel wheel profile designing method, which combines FaSrtip, wheel material loss function developed by University of Sheffield (USFD function), and Kriging surrogate model (KSM), to reduce wheel wear for these vehicles that primarily operate on fixed lines, for which an Sgnss wagon running on the German Blankenburg–Rübeland railway line is introduced as a case. Besides, regarding the influence of vehicle suspension characteristics on wheel wear, most of the studies have studied the lateral stiffness, longitudinal stiffness, and yaw damper characteristics of suspension systems, since these parameters have an obvious influence on wheel wear. However, there is currently little research on the relationship between the vertical suspension characteristics and wheel wear. Therefore, it is also investigated in this paper, and a suggestion for the arrangement of the vertical primary spring stiffness of the Y25 bogie is given.TU Berlin, Open-Access-Mittel – 2020EC/H2020/826250/EU/Measuring, monitoring and data handling for railway assets; bridges, tunnels, tracks and safety systems/Assets4RailEC/FP7/234079/EU/Railway Vehicle Dynamics and Track Interactions - Total Regulatory Acceptance for the Interoperable Network/DYNOTRAI