Track Geometry Irregularities Research Articles

Vibration-Based Detection of Axlebox Bearing Considering Inner and Outer Ring Raceway Defects

The occurrence of an axlebox bearing ring raceway defect is an inevitable and commonly observed phenomenon in railway wheels. It not only leads to surface damage but also poses the potential threat of further damage and degradation, thereby increasing the risks associated with running safety and maintenance costs. Hence, it becomes imperative to detect raceway defects at an early stage to mitigate safety hazards and reduce maintenance efforts. In this study, the focus lies in investigating the effectiveness of vibration-based detection techniques for identifying raceway defects in high-speed train axlebox bearing systems. To achieve this, a dynamic model that accurately represents the coupling dynamics between the vehicle and the track is developed. This model incorporates various dynamic factors, such as traction transmission, gear transmission, and track geometry irregularities. By using the comprehensive dynamic model, the dynamic responses of the axlebox can be accurately calculated. The proposed methodology primarily revolves around analysing the vertical vibrations of the axlebox caused by raceway defects in both the time and frequency domains. Additionally, an envelope analysis using a developed band-pass filter is also employed. The results obtained from this study clearly demonstrate the successful detection of raceway defects in a more realistic vehicle model, thereby providing an efficient approach for the detection of axlebox bearing raceway defects. Consequently, this research contributes significantly to the field of high-speed train systems and paves the way for enhanced safety and maintenance practices.

Dynamic calibration method for track geometry measurement system-a case study in China

With the rapid development of railway construction, the mileage of railway detection has increased dramatically, and railway companies have higher requirements for the repeatability and accuracy of track geometric dynamic detection data. Therefore, the track geometry measurement system needs to be calibrated to improve the measurement accuracy to ensure the safety of railway operation. However, the existing calibration methods of the track geometry measurement system can only realize the static calibration and lack the dynamic calibration method, which cannot calibrate the track geometry measurement system under the real operation state. Therefore, a dynamic calibration method for the track geometry measurement system is proposed in this paper. A real and reliable test environment is provided for the track geometry measurement system by presetting the track geometry irregularity and laying the rail dynamic displacement monitoring system on the calibration test line, and the dynamic and static differences of the track are analyzed based on the test environment. Based on the bisection method, this paper reduces the measurement error of the track geometry measurement system and analyzes the measurement repeatability of the track geometry measurement system. Finally, the error correction model of the track geometry measurement system is established, and the error analysis and uncertainty evaluation of the track geometry measurement system are carried out. The results show that the track geometry measurement system calibrated by the dynamic calibration method proposed in this paper meets the requirements of the measurement error for the gauge and cross level. This study can establish the value traceability relationship of the track geometry measurement system, realize the calibration and evaluation of the accuracy of the track geometry measurement system under the real operation state, and improve the reliability and accuracy of the track geometry dynamic detection results.

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.

The effect of the wavelength of lateral track geometry irregularities on the response measurable by an instrumented freight wagon

Condition monitoring of railway track geometry using instrumented in-service vehicles has been the subject of extensive research aimed at replacing or enhancing the data provided by dedicated track recording vehicles. Lateral track irregularities present difficulties compared to vertical irregularities due to the lateral clearance between the wheel flanges and the rail head. Wheelsets don’t necessarily follow the lateral trajectory of the track. Simulations of a freight wagon with three-piece bogies negotiating both discrete and cyclic lateral track irregularities showed that the resulting lateral displacements were highly dependent on the wavelength of the irregularities. The wheel/rail profiles also had a significant effect whilst the effect of speed was relatively minor. These findings highlight the complexities of identifying exceedances of track geometry standards using only inertial sensors on instrumented freight wagons.

Spatiotemporal forecasting of vertical track alignment with exogenous factors

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, a method is proposed to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory. The proposed method is also experimentally compared with other methods in terms of the forecasting performance. Additionally, an ablation study on exogenous factors is conducted to examine their individual contributions to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of vertical alignment.

Investigation of Track Gauge and Alignment Parameters of Ballasted Railway Tracks Based on Real Measurements Using Signal Processing Techniques

This paper deals with the time-frequency characteristic analysis for track geometry irregularities using field data recorded by a comprehensive track inspection train. The parameters of the track gauge and the left and right rail alignment are considered to identify their characteristic wavelengths and the locations of their waveforms. In addition to the conventional time and frequency domain analysis, auto-adaptive signal decomposition techniques are used on four pre-selected track sections. During the time series analysis of the track gauge, the cumulative difference from the mean value is calculated, which makes it possible to distinguish the track section constructed with non-standard initial track gauges. The sensitive wavelengths of the track irregularities are obtained from the proper allocation of wavelength ranges in the Fourier Amplitude Spectrum of the original signal and the Fourier transform of the components detected by the Variational Mode Decomposition. This analysis can elucidate the wavelengths and positions of track irregularities that affect vehicle responses.

Estimation of Vehicle Dynamic Response from Track Irregularity Using Deep Learning Techniques

To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. This paper proposes a deep learning model to predict vehicle responses (e.g., vertical wheel-rail forces, wheel unloading rate, and car body vertical acceleration) using deep learning techniques. In the proposed CA-CNN-MUSE model, convolutional neural networks (CNNs) are used to learn features of track irregularities, and multiscale self-attention mechanisms (MUSE) are employed to capture the long-term and short-term trends of sequences. Coordinate attention (CA) is introduced into CNN to focus on important interchannel relationships and important spatial mileage points. The experiments were performed on a multibody simulation model of the vehicle system and the measured data of the actual high-speed line. The results show that the CA-CNN-MUSE has high prediction accuracy for vertical vehicle responses and fast computation speed. The predicted time-domain waveforms and power spectral densities (PSDs) agree well with the actual vehicle responses. The main features of the lateral vehicle responses can also be captured by the proposed method, yet the results are not as good as the vertical ones.

Identification of Subway Track Irregularities Based on Detection Data of Portable Detector

At present, the detection of subway track irregularities is mainly carried out by track inspection vehicles and track inspection trolleys. Such detections are restricted by subway service time, so they can only be carried out once every few months. This study explored the possibility of using the vibration of the vehicle body collected by a novel portable detector to detect track geometry irregularities. It makes a particular contribution to the dynamic detection of track conditions and the reduction of maintenance costs. Based on the data collected by the portable detector, wavelet transform was used to analyze the vibration of the vehicle body. The results confirmed that this method was effective in enhancing the correlation between vibration accelerations and track irregularities. Second, a data set processed by wavelet transform was resampled by a hybrid sampling method which uses clustering methods and considers data imbalance within each category. In this way, the imbalance ratio of the data set was found to be reduced without changing the original data set structure. Finally, the random forest algorithm and the gradient boost decision tree algorithm were adopted for classifying track regularity and irregularity data. The results showed that both two algorithms, especially the random forest algorithm, performed well for the longitudinal level track irregularity and the alignment track irregularity.

Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique

Track geometry monitoring is essential for track maintenance. Dedicated track inspection vehicles are scheduled to measure track geometry irregularities throughout the railway network, so cannot inspect each line frequently. It is desirable to inspect geometry much higher frequently using in-service trains. One possible way is estimating track geometry from vehicle–body accelerations because the accelerators can be easily installed in vehicle–body. However, inverting track geometry from vehicle–body acceleration is a pending issue. Up to now, most research has focused on vehicle dynamics modelling and simulation. In this paper, we solve the problem using deep learning method and realistic measurement data. The training and test data are the inspection data acquired by comprehensive inspection trains from three main high-speed railways in China. The proposed AM–CNN–GRU model combines an Attention Mechanism (AM), a Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). The model’s inputs are vertical and lateral accelerations, and vehicle speed. The outputs are two vertical track irregularities, with wavelengths of 3–42m and 3–120 m, respectively. We evaluated the model by comparing the estimated irregularities with the actual measurements. We discussed different models, high-speed lines, sub-rail infrastructures and speed to validate the model effectiveness.

Estimation of railway track longitudinal irregularity using vehicle response with information compression and Bayesian deep learning

AbstractIn railway transportation, track geometry irregularity is one of the main factors in controlling train safety. At present, railway practitioners typically use the track geometry car (TGC) based on the inertial navigation system to inspect track irregularities. However, TGCs are quite expensive, and their inspection interval is relatively long. Among a variety of emerging methods, using vehicle responses to estimate track irregularities seems very promising as it enables a cheaper and more efficient solution. In this work, an extended auto‐encoder (EAE) is proposed to estimate the track longitudinal irregularity through car body acceleration. The mean absolute percentage errors of the estimated results on the simulated and the real‐world dataset are 2.67% and 3.75%, respectively, which is 50%–55% lower than the traditional neural network. In the frequency domain, the characteristic wavelengths of 5.4 and 32 m can be effectively identified. Besides, the Bayesian deep learning (BDL) method is introduced to improve the EAE and estimate the confidence interval of the track longitudinal amplitude. A metric (coverage width and error) for evaluating and optimizing the performance of the estimated interval is proposed. The interval estimation result in the time domain has a 98% correct coverage rate of the ground truth and 93% in the frequency domain. Within the error range of plus/minus one standard deviation, the EAE model has an estimation accuracy of 94.2% for the standard deviation of track longitudinal irregularity, and the BDL‐EAE can even reach 100%. Compared with the existing methods, our proposed model only requires car body acceleration and has the potential to use ordinary in‐service trains for onboard track inspection.

A reliability analysis method of dynamic irregularity for track–viaduct system with low stiffness

To evaluate the reliability of the dynamic irregularity of a track structure in satisfying the safety requirements, a stochastic virtual track inspection (SVTI) system that can be used to predict the time-varying probability distribution of dynamic irregularity was developed by combining virtual track inspection (VTI) system and the generalized probability density evolution method (GPDEM) in this study. A train–track–bridge dynamic interaction model (TTBDIM) was built using the Universal Mechanism platform. The inertial reference method (IRM) was simulated by finding the frequency-domain quadratic integration of the axle box acceleration and filtering it, based on which the deterministic VTI system was established. For the sake of probability analysis, the track geometry irregularities (TGI) was considered as a stochastic variable. A number-theory-based uniform experimental design was used to determine the representative points in the high-dimensional probability space. SVTI was established by coupling the deterministic VTI system and the generalized probability density evolution equation. The probability density evolution function of the dynamic irregularity was solved for using the finite difference method with the total variation diminishing (TVD) format. In this study, floating slab tracks on a U-shaped beam viaduct were taken as an example to analyze the influence of track stiffness irregularity (TSI) on the basic signal characteristics of the dynamic irregularity of the tracks using the deterministic VTI system. Furthermore, the SVTI system was used to analyze the influence of TSI on the time-varying probability distribution and determine whether the dynamic inspection requirements of the dynamic irregularity were satisfied. The research findings suggested that the TSI of the track–viaduct system with a low stiffness had a significant influence on the time-varying characteristics of the dynamic irregularity probability distribution. As the stiffness of the track–viaduct system decreased, the mean probability distribution of the dynamic irregularity extrema shifted to the higher-level area with an increasing standard deviation. When rubber spring floating slab tracks (RSFSTs) and steel spring floating slab tracks (SSFSTs) were laid on the 35-m-span U-shaped beam, the probabilities of dynamic irregularity exceeding the limit were 16.13% and 30.02%, respectively. The deterministic VTI system might not be able to detect irregularities exceeding the limit within the limited simulation mileage, which could lead to misjudgments and track regularity overestimation. In conclusion, the proposed SVTI system will provide direction for the design of track–viaduct systems with low stiffnesses in urban rail transit construction.

Determining the critical speed for hunting of three-piece freight bogies: practice versus simulation approaches

Hunting causes accelerated wear and damage to bogies, track and the lading carried by railway wagons and can, in conjunction with track geometry irregularities, lead to derailment. Simulation can be used to estimate the critical speed below which sustained hunting will not occur given certain parameters of the wagon, track and lading. This paper investigates the hunting of an unloaded freight wagon fitted with three-piece bogies which utilise friction wedges for damping and have friction at many interfaces. Several methods of analysis are compared including simulation on track with irregularities. The novel use of different initial conditions at each bogie is shown to excite different types of hunting at a given speed. Only one of these was excited by the common method of running over a discrete lateral track irregularity.

Measurement Distortion Analysis of Repetitive and Isolated Track Geometry Irregularities

Track geometry measurements are essential for day-to-day activities of railway maintenance and play an important role in vehicle-track simulations. The generally applied forms of longitudinal level and alignment recordings do not reflect the real shape of the track. Both the versine measurement method and the band-pass filters according to European regulation cause significant amplitude modification and pattern change. In addition, the distortion behavior of repetitive and isolated defects is fundamentally different. In this contribution, simulated measurements of various reference shapes, which represent repetitive and isolated track deformations, were investigated. Comprehensive functions for amplitude change and for other distortion factors were developed with analytical and numerical methods. For chord measurements, rules were found for zero points and distortion-free ranges. Regarding the standardized filters, a significant amplitude reduction of isolated defects was observed in all wavelength ranges. Since derailment and track degradation depend not only on the amplitude of the defect, also the derivatives of the original and filtered forms of reference shapes were investigated and, as a new approach, the defect features called 'hypothetical additional force', 'speed of hypothetical wheel lift-off', 'hypothetical deterioration impulse' and 'hypothetical deterioration energy' were introduced.

Evaluation of Shape Array sensors to quantify the spatial distribution and seasonal rate of track settlement

Rail track geometry irregularities can lead to ride discomfort for passengers and redistribution of wheel loads potentially causing derailments. Current techniques for monitoring track settlements involve the use of discrete sensors or vehicle mounted sensors, which make it difficult to capture either spatial or temporal variations in settlement. Shape Array sensors (SAA) can potentially be used to capture temporal variations in distributed track settlement profiles to monitor and investigate potential track geometry irregularities and inform track maintenance programs. In this study, a site with known ground deformation issues (i.e. soft spots) was monitored with both an SAA and intermittent LIDAR scans. The objectives of the research were to investigate the accuracy of the SAA for measuring track settlements based on a comparison with LIDAR data, use those measurements to assess track irregularities at the site, evaluate temporal and seasonal changes in rail deformation, and to gain insight into the underlying causes of the ground deformation issues. The SAA was found to provide comparable settlement measurements to those from the LIDAR with the added advantage that the data could be used to assess settlement rates. At this particular site, long-term rail settlements were found to be a function of seasonal/climatic conditions with slower settlement rates in the winter and higher rates in the summer. In addition, the measurements indicated that the deformation issues are potentially caused by an asymmetric bearing failure.

Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities

Tracks are critical and expensive railroad asset, requiring frequent maintenance. The stress from heavy car axle loads increases the risk of deviations from uniform track geometry. Irregularities in track geometry, such as track warping, can cause an excessive harmonic rocking condition that can lead to derailments, traffic delays, and associated financial losses. This article presents an approach to enhance the location identification accuracy of track geometry irregularities by combining measurements from sensors aboard Hi-Rail vehicles. However, speed variations, position recording errors, low GPS update rates, and the non-uniform sampling rates of inertial sensors pose significant challenges for signal processing, feature extraction, and signal combination. This study introduces a method of extracting features from the fused data of inertial sensors and GPS receivers with multiple traversals to locate and characterize irregularities of track geometry. The proposed method provides robust detection and enhanced accuracy in the localization of irregularities within spatial windows along the track segment. Tradeoff analysis found that the optimal spatial window size is 5-meter.

Vertical track irregularity analysis of high-speed railways on simply-supported beam bridges based on the virtual track inspection method

Understanding the relationship between the static and dynamic track geometry irregularities is crucial for the proper maintenance of rail infrastructures and the reduction of on-site workload. This paper focuses on the analysis of the dynamic and static track irregularities on simply-supported beam bridges for high-speed railways. Based on the simulation of three-dimensional vehicle–track–bridge dynamics, a virtual track inspection method is proposed according to the measurement principle with the inertial reference. With the static irregularity provided as the initial input to the simulation model, the virtual track inspection of dynamic track irregularities is carried out considering different supporting structures, i.e. subgrade and bridges. Furthermore, the characteristics and advantages of the proposed model are investigated in the “rigid track structure”. Then, using the virtual track inspection method, this paper analyzes the relationship between the dynamic and static track irregularities (in the vertical direction) on the simply-supported beam bridge in both the time and frequency domains with respect to different train speeds, and the simulation results are validated by real-world measurements. Numerical results show that the stiffness irregularity in the vertical direction is periodical, with the cycle length equal to the span of the bridge. Furthermore, there is an obvious linear relationship between the dynamic and static irregularities. Also, the regression coefficient increases with increasing vehicle speed.

Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms

In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.

Time–frequency characteristic analysis method for track geometry irregularities based on multivariate empirical mode decomposition and Hilbert spectral analysis

The description and evaluation of track geometry irregularities are commonly performed in the distance domain or the frequency domain, and therefore one cannot simultaneously obtain precise measurements of actual frequencies and the distance locations where they appear. This paper proposes the time–frequency energy density (TFED) method to analyse the characteristics of track irregularities in the joint spatial distance–frequency plane. The proposed method describes the energy density distribution of track irregularities in the spatial distance–frequency domain based on the adaptive decomposition characteristics of the multivariate empirical mode decomposition and the time–frequency aggregation on the signal energy of the Hilbert spectrum. The mathematical relationships between the TFED and the standard deviation, as well as the power spectral density, are deduced and validated in the field data of track irregularities. The TFED method was applied to the spatial distance–frequency characteristic distribution of track irregularities and the distribution of instantaneous energy density in the sensitive wavelengths. The track geometry quality is evaluated based on the TFED, using track inspection data of actual high-speed railways.

Analysis of Vehicle Dynamic Performance under MCAT Excitation

In order to validate the running safety on track of railway vehicle, a numerical analysis is performed with a multi-body vehicle dynamics model built. Follow the vehicle-track interaction (VTI) evaluation declared in north America standard declared by Federal Railway Association (FRA), a Minimally Compliant Analytical Track (MCAT) worthiness analysis is employed. In this investigation, the analytical track geometry irregularities which consist of surface, vertical and short warp are defined. Then the running performances concerning the derailment safety of the vehicle is discussed by evaluating the wheel unloading and derailment coefficients. Vibration acceleration of the carbody is also monitored to evaluate the ride comfort. By changing the track irregularities MCAT, the impact of track wavelength, gauge and cant deficiency on the dynamic performance of the vehicle can be assed.

The influence of variation in track level and support system stiffness over longer lengths of track for track performance and vehicle track interaction

Differential settlement and development of track geometry irregularity drives the need for maintenance of ballasted railway tracks. Predicting this requires an understanding of how train loading and the resulting stresses vary and are distributed along the track and how the track responds. Irregularities, from differences in the unloaded level and deflection under load (from variation in stiffness and load) influence the wheel-rail contact force along the track. The stiffness will also influence the distribution of stress into the ground. To investigate variation in the (unloaded) track level and support system stiffness along a railway, stiffness, track deflection and sleeper level surveys were carried out along a 200 m length of track. Measurements were taken at every sleeper using total station for track level and accelerometers to calculate deflection, together with a frequency-based analysis for the track support system stiffness. A simple 2D vehicle track interaction model was used to study the influence of the unloaded sleeper level, the variation in track stiffness and any identified voiding for the performance of the track. Here, the unloaded level was more significant for the loaded level and the wheel / rail contact forces than the variation in track stiffness, which was most importance for the deflections under load.