Track Geometry Inspection Research Articles

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities

ABSTRACT Ballast layer defects are the primary cause for rapid track geometry degradation. Detecting these defects in real-time during track inspections is urgently needed to ensure safe train operations. To achieve this, an indicator, the track degradation rate (TDR) was proposed. This rate is calculated using track geometry inspection data to locate and predict railway-line sections with ballast layer defects. The TDR is determined by the monthly standard deviation of the rail longitudinal level, which is one aspect of track geometry. The Ballast Layer Health Classification (BLHC) is designed by assessing the two successive TDRs before and after track geometry maintenance actions. The BLHC is used to categorize the conditions of the ballast layer, including normal periodic deterioration, abrupt deterioration, effective maintenance, rising deterioration, and severe deterioration. Both the TDR and BLHC were validated through field assessments of ballast layer conditions, where the two indicators were found to be effective in revealing defects. The results indicate that the TDR is sensitive to ballast layer defects, while the BLHC can quickly identify the location of these defects. Consequently, the BLHC can provide real-time guidance for ballast layer maintenance.

Similarity based position calibration for multisource track inspection data

Track geometry inspection data have long endured positional errors and shifts, hindering the quality evaluation, deterioration analysis and maintenance work of railway track. Besides, vehicle response inspection data are also typically misaligned or shifted from each other, but it tends to be ignored by people. In light of this situation, a position calibration method on the basis of a simple similarity metric, i.e. correlation coefficient, is proposed to tackle the positional errors and shifts among multisource track inspection data concurrently. For the convenience of practical implementation, a scheme containing three parts is designed to fulfil the purpose and a two-step procedure is adopted by all the three parts. According to the performance test on a dataset containing five inspection runs of a high-speed railway in China, the positional error between inspection data and actual locations can be greatly diminished, and the data inspected at different times and from different sources are well aligned with each other, with a time consumption of 0.66 s on average for a 1 km segment.

Main Factors on Effect of Precise Measurement and Precise Tamping Based on BP Neural Network

With the continuous development of precise measurement and precise tamping (PMPT) technology on Chinese railway conventional speed lines, the efficiency of machinery tamping operation and the quality of the track have been effectively improved. A variety of PMPT modes have been tried in the field operation, however there are some differences in the operation effect. The quality of the tamping operation is affected by multiple factors. In order to identify the key factors affecting the operation quality and to further improve the tamping operation effect, this paper establishes both the database of PMPT operation modes and the selection index system for evaluating the operation effect. Based on mega multi-source heterogeneous data and track geometry inspection data, this paper adopts the Back Propagation Neural Network (BPNN) prognosis model to quantify and sort the main factors affecting the effect of PMPT. The research results show that the initial quality of the track before tamping, whether the stabilizing operation or the tamping modes have great influence weights. It can scientifically guide the field operation to control the key factors and put forward some practical suggestions for promoting the field application of PMPT and the optimization of operation modes on the conventional speed lines.

Identifying Parametric Models Used to Estimate Track Irregularities of a High-Speed Railway

This study aims to identify parametric models to estimate track irregularities in high-speed railways with simple acceleration measurements. The primary contribution of current research is the development of effective parametric models with smaller parameters. These parameters are derived from the measured data via a specialized track geometry inspection system. An adaptive Kalman filter algorithm, using the displacement estimated from the acceleration signals as the input and measured track irregularities as the output, is applied to obtain the model’s unknown parameters. These models are applied to acceleration measured from high-speed rail vehicles in operation, and track irregularities are estimated in spatial and wavelength domains. The estimated irregularities are compared to the track geometry inspection system’s results.

Sequential State and Unknown Parameter Estimation Strategy and its Application to a Sensor Fusion Problem

Soft-sensing and state-space-based methods play crucial roles in fault-tolerant sensor fusion schemes. Kalman filtering is one of the most employed sensor fusion methods. In practice, filtering (or state estimation) is sensitive to initial conditions, variations in model parameters, and faults in sensors and actuators. Cumulatively, these uncertainties that affect the state estimator can be modeled as unknown parameters. In this article, we present the development of a discrete-time minimum variance unbiased estimator (MVUE) that is robust to a class of unknown parameters. The estimation gain of MVUE is designed to facilitate unbiased state estimates in the presence of unknown parameters. We then use the innovations that are generated by the proposed state estimator (MVUE) to estimate the unknown parameters, thus proposing a sequential state and unknown parameter estimation strategy. The development of the decoupled state and unknown parameter estimation strategy is motivated by the fault-tolerant sensor fusion exercise for railway track geometry inspection. This exercise aims to formulate the state-space realization of the track geometry inspection problem to enable sequential state and unknown parameter estimation. The track geometry parameters, initial conditions, and sensor faults are considered unknown parameters for the application. The simulation analysis shows the importance of developing the trinity of the state-space realization for track geometry inspection, the state estimation framework, and the decoupled unknown parameter estimator.

Evaluating Rail Surface Roughness from Axle-Box Acceleration Measurements: Computational Metrology Approach

This study develops a new methodology based on computational metrology techniques to measure rail surface roughness from vertical axle-box accelerations. A VIA rail passenger rail car, operating in eastern Ontario, Canada, was instrumented with accelerometers on the car body, trucks, and axle-boxes. The rail surface was measured by a heavy track geometry inspection car. A Gaussian filter was applied to the measured rail surface data and the rail surface roughness was quantified by the root mean square (RMS), as recommended by ISO 11562 for evaluating engineering surfaces. Values of rail surface RMS roughness calculated from axle-box accelerometer data are verified with the measured surface RMS roughness. The overlap ratio and length of the moving window over which the rail surface RMS roughness is calculated are studied with respect to roughness wavelengths, statistical considerations, and maintenance planning purposes. The effect of rail car operating speed as well as the difference between axle-box accelerations measured at two axles of the instrumented rail car on the accuracy of estimating the rail surface RMS roughness are assessed. Filtering techniques and application limitations for calculating roughness are also discussed. The results of this study suggest axle-box acceleration data provide a useful assessment of rail surface roughness for the typical wavelengths between 3 and 25 m and a complementary technique to light and heavy track geometry inspections.

A Machine Vision System Based on Driving Recorder for Automatic Inspection of Rail Curvature

Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail track condition efficiently and accurately. In this article, a machine vision system based on driving recorder and image signal processing is proposed to evaluate the rail curvature automatically. The proposed machine vision system consists of four modules including the video acquisition module, the image extraction module, the image processing module, and the track condition assessment module. Three classic edge detection methods are adopted and compared for rail edge detection. In line with the videos of driving recorder, coordinate systems for train and rail are defined in the Lagrangian space, and the track curvature is estimated using the proposed chord offset method and double measurement method. For evaluating the track condition, an index describing the concordance between the train and track is defined. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique, and the obtained results are verified by comparison with those obtained by a track geometry inspection vehicle. It is shown that the proposed technique can evaluate the track curvature accurately. Moreover, the influence of the position of deployed driving recorder, the focal length and anti-shake of camera on the accuracy of evaluation results is discussed. It is testified that the proposed technique provides a simple and reliable way to inspect the track curvature.

Assessment and prediction of high speed railway bridge long-term deformation based on track geometry inspection big data

This paper proposes a low-cost, data-driven approach to assess and predict bridge deformation using track inspection big data, which is primarily used for assessing track conditions. Firstly, a Bridge Deformation Assessment model with a sophisticated signal processing process is introduced to manipulate track geometry inspection data for extracting bridge-related components. Secondly, a Bridge Dynamical Deformation index (BDD index) is defined to quantify bridge deformation based on track geometry inspection data. Thirdly, the Temperature-Time-Deformation model (TTD model) is established to describe bridge deformation with respect to ambient temperature and length of service time of the bridge. Three types of TTD equations are proposed, including exponential-, hyperbolic- and linear-TTD equations. Fourthly, a track geometry inspection dataset over 2.6 years involving 563 bridge spans is applied as a case study. It is found that the BDD index changes with ambient temperature by 0.02 mm/°C on average, and increases with time by 0.2 mm/year during the 2.6-year period. Furthermore, a prediction on the amount of increase of the BDD index over the following 3 years is given with a 95% confidence level. It is expected that BDD index will increase by 0.5 mm in 2 years and 0.7 mm in 3 years according to the TTD model. Finally, the model uncertainty is discussed from data aspect and model aspect. The methods in this paper are of reference value for research topics on bridge condition evolution, rail geometry degradation and prediction-based infrastructure maintenance.

Effective time interval for railway track geometry inspection

A proper decision-making scheme for track geometry maintenance requires a knowledge of the real condition of track geometry. Therefore, the track must be inspected by measurement cars at different time intervals. The frequency of track geometry inspection plays a crucial role in decision-making and has always been a big concern for infrastructure managers. The inspection interval should be chosen properly, it means that the small period can decrease the capacity of line and affect the operation of network and the big period can result in low quality of track and in some cases derailments and possible loss of human lives. The aim of this paper is to determine the effective inspection interval such that the total maintenance cost is minimized. In the proposed cost model, the costs of inspection, preventive maintenance, corrective maintenance and the penalty for exceeding the corrective maintenance level are considered. A case study is performed on a real dataset collected from a railway line in Iran. The standard deviation of longitudinal level is considered to measure track geometry degradation. A widely applied linear model is used to model track geometry degradation over time. Monte Carlo technique is used to simulate the track geometry behavior under various track geometry inspection intervals. In addition, a set of sensitivity analyses are carried out to assess the effect of various inspection intervals on different terms of maintenance cost. The results indicate that not only can substantial costs be saved by setting effective inspection intervals, but also the time during which the track suffers from bad conditions is dramatically reduced. The result of this study has shown the appropriate inspection interval for the studied case can result in 13.6 percent decrease in maintenance cost in comparison with the current maintenance policy. Besides, it would lead to more reliable railway track by preventing the system exceed the corrective threshold.

Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.

A threshold-regression model for track geometry degradation

Track-related failures are a major factor contributing to train derailments in the United States. Therefore, determining the failure time is critical for safety purposes. Traditionally, failure time in track geometry has been modeled using defect data. However, unless it is an accident due to extreme events, track geometry fails as a result of an underlying degradation process. The first hitting time is referred to the probability distribution of the time at which the degradation path first reaches a safety threshold. This paper presents the formulation and implementation of the first hitting time in railway track geometry degradation using track geometry inspection data. The underlying degradation path is modeled as a Wiener process with drift, and the first hitting time follows an inverse Gaussian distribution. The results provide a more robust representation of the failure time in track geometry using degradation data.

<b>Recent Trends in Track Inspection and Monitoring Technologies</b>

Track inspection and monitoring systems are one of the most important technologies for track maintenance. For effective and labor-saving track maintenance, several types of new system or device are being developed. In Europe in particular a number of European Norms for track inspection have been established to standardize technology so that it can be exported around the world. In this paper, the author explains recent and future trends of track inspection and monitoring technologies, with a special focus on track geometry inspection and frog defect detection systems.

Wu Guang High-Speed Rail Track Irregularity Power Spectrum Analysis

Irregularity is the locomotive and the main excitation source of vibration, is directly related to the smooth running of the train, safety and comfort, is to control the maximum operating speed of the train one of the main factors. The statistic specimen was collected by track geometry inspection car from Wuhan-Guangzhou high-speed rail. Based on the stationarity test of the specimen, Fast Fourier Transform (FFT) method was used to evaluate the spectrum of the whole specimen space. The power spectrum density (PSD) and related functions of track irregularity were obtained by MATLAB program. We analyzed it by comparing the fitting curve of the national speed lines and fitting curve of Qinhuangdao-Shenyang dedicated passenger railway line, by comparison, the Wuhan-Guangzhou high-speed railway track irregularity is much better than the national speed lines and Qinhuangdao-Shenyang dedicated passenger railway line. The parameter values of PSD fitting curve for track irregularity are obtained by the nonlinear curve-fitting algorithm in the least-squares sense, which has referencing value to maintenance high-speed rail.

Optimisation of track geometry inspection interval

The measurement and improvement of track quality are key issues in determining the time at which railway maintenance must be performed and its cost. Efficient track maintenance ensures optimum allocation of limited maintenance resources which has an enormous effect on maintenance efficiency. Applying an appropriate tamping strategy helps reduce maintenance costs, making operations more cost-effective and leading to increased safety and passenger comfort levels. This paper discusses optimisation of the track geometry inspection interval with a view to minimising the total ballast maintenance costs per unit traffic load. The proposed model considers inspection time, the maintenance-planning horizon time after inspection and takes into account the costs associated with inspection, tamping and risk of accidents due to poor track quality. It draws on track geometry data from the iron ore line (Malmbanan) in northern Sweden, used by both passenger and freight trains, to find the probability distribution of geometry faults.

A new approach to the analysis and presentation of vertical track geometry quality and rail roughness

A new approach on enhancing the assessment of track geometry quality and rail roughness by means of train–track interaction simulation and wavelength content analysis is presented. The dynamic model includes vehicle, track and linearized wheel–rail contact with moving irregularities and can be simulated either in the frequency domain by using FFT or in the time domain by constructing a filter function based on system identification technique. The system is suitable to calculate wheel–rail forces for very long track sections and for several vehicle types under a wide range of travelling speeds since the computational scheme is very efficient (300 km/s on a standard computer). With numerical results we demonstrate the potential benefits of improving conventional track geometry inspection methods and highlight short defects (0.5–2 m) as a cause of high dynamic wheel–rail interaction forces. By using a wavelength weighting of measured rail roughness a new improved way of analyzing rail roughness data is also presented. This improves condition assessment of tracks and rails and will enable the track engineer to monitor the track in a better way.

Relating track geometry to vehicle performance using neural network approach

As part of a research that led to the development of the performance-based track geometry (PBTG) inspection technology, the Transportation Technology Center, Inc., Pueblo, CO, USA, a wholly owned subsidiary of the Association of American Railroads, has developed a technique relating track geometry to vehicle performance, real-time. This technique is based on the neural network (NN) approach, an emerging powerful tool in recognizing complex patterns and non-linear relationships between many inputs and an output, such as the relationship between track geometry and vehicle response. On the basis of this technique, many NNs have been developed (trained) from actual vehicle/track interaction test results. For a given vehicle type, the trained NNs directly relate three-dimensional track geometry and vehicle operating speed to vehicle performance. The effects of other track conditions such as lubrication, rail profile, and track stiffness are indirectly considered on the basis of their statistical distributions from test results. Because the PBTG inspection is intended to optimize the track geometry maintenance, the vehicle types selected for testing to date were the ones most sensitive to track geometry (in North America, these are the tank car, covered hopper car, and coal gondola car). However, more NNs for other vehicle types can be easily trained on the basis of the NN technique developed using actual vehicle performance and track geometry test results.