Rail Profile Research Articles

Development of hunting oscillation detection algorithm for railway vehicles by using accelerometers

In this study, an algorithm was developed to detect hunting oscillation in railway vehicles using only accelerometers. Typically, the wheel profile of most railway vehicles has a conical shape, causing inherent hunting oscillation. The critical speed at which hunting oscillation occurs is determined by the wheel profile, rail profile, and dynamic characteristics of the railway vehicles. Hunting oscillation significantly impairs ride comfort, reduces running stability, and increases the risk of accidents such as derailment. Therefore, if hunting oscillation occurs during railway operation, it must be detected, and a deceleration procedure should be applied immediately to ensure dynamic stability. The detection algorithm in this study is based on the geometric behavior of the bogie. It utilizes longitudinal and lateral acceleration data from accelerometers installed on the upper frame of the bogie above the axle boxes. The algorithm processes the acceleration data to obtain the hunting oscillation frequency and harmonics, integrating all this data to calculate the Index of Hunting Oscillation. Validation was performed through simulations using a standard electric train model, confirming the algorithm’s effectiveness. Additionally, tests on actual bogies using a roller rig validated the algorithm’s performance in detecting hunting oscillation effectively.

A high-precision visual detection method for rail profile based on Scheimpflug condition

Rail profile is an important indicator of rail service status, which should be always kept in good condition. How to efficiently collect rail profiles with high precision is a critical issue for their condition-based maintenance. This paper focuses on the method for accurate visual detection of rail full profile (RFP), including optical modeling, visual calibration, and error compensation. The proposed optical unit, featuring double line structured-light sensors, introduces an innovative method under the Scheimpflug condition to avoid the limitation of depth of field in RFP measurement. Subsequently, double visual sensors are calibrated together using the common reference target to extract the internal and external parameters respectively. The dynamic errors resulting from the impact of vehicle vibrations are ultimately investigated to rectify the distorted profiles to the normal. Both laboratory and field tests are performed to verify the effectiveness of the proposed system in terms of precision and efficiency.

Laser Scan Compression for Rail Inspection

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans.

A case study of railway curve squeal radiated from both the outer and inner wheel

This case study is presented after observations were made that partially contrast the prevailing picture of railway curve squeal given in the literature. These are observations with significance both for modelling and condition monitoring. The current case study will be followed by a subsequent investigation focused on the validation and application of a simulation model to investigate root-causes for curve squeal at the Stockholm metro. Curve squeal in a 213 m radius curve on the Stockholm metro is studied. Data includes track characteristics (rail profile, rail roughness and gauge width) and noise measured at the trackside as well as by two vehicles equipped with an on-board mounted noise monitoring system. The current case study contrasts field measurements of curve squeal reported in the literature by a relative shift of emitted noise towards higher frequencies. Wayside noise measurements in the studied curve during a few hours showed squeal generation for all vehicle passages with dominating 1/3 octave band centre frequencies in the range between 6.3–15.8 kHz. Noise data measured during one year of regular traffic of two vehicles equipped with a monitoring system were obtained. The occurrence of curve squeal was analysed through an implementation of the curve squeal detection algorithm in operation at the Stockholm metro. This algorithm was also applied to search for events of squeal noise radiation from the outer wheel. Results show emissions of squeal noise from the inner and outer wheel for 65 % and 8 % of the vehicle passages through the studied curve, respectively. Further, the occurrence of curve squeal radiated from the inner wheel was found to increase by 10 % after rail grinding. In the literature, squeal radiated from the outer wheel is described as having an intermittent character with magnified spectral components in the frequency range between 5–10 kHz. In contrast, the current work presents sustained tonal squeal generated from the outer wheel with similar noise characteristics as typically related to ordinary curve squeal.

Wheel-rail wear characteristics of a modern tram passing curves with small radius

The wheel-rail wear was predicted for modern trams with independently rotating wheelset running on a groove-shaped rail. The tram vehicle-track coupled dynamics model, considering the wheel-rail multi-point contact, was developed using software UM to evaluate the dynamic responses of the vehicle and track. Hertz contact theory, the FastSim algorithm, and Archard material wear model were used to solve the normal contact stress, tangential contact stress, and wheel-ail wear, respectively. The wheel and rail profiles were updated when the wear depth reached to 0.1 mm. In calculation, the wheel and rail wear was calculated and analyzed separately. The results of groove rail and independently rotating wheelset wear predicted by this model coincided with relevant literature, which proved the effectiveness of this model. The wheel and rail wear characteristics of modern trams passing through curved tracks with small radii and the influence of wheel-rail friction coefficient on wheel and rail wear were calculated and analyzed using this model. The results show that the wheel and rail wear on the circular curve and the rear transition curve is more intense. The wheel wear is the most intense on circular curve while the rail wear is on the rear transition curve. The wear of inner and outer rails and wheels of wheelset one increases with the increase of wheel-rail friction coefficient while the wear of inner and outer wheels of wheelset three have no obvious change. The wear of the inner and outer rails is the most intense at the gauge corner position while is the lightest at the guard rail. The wear of wheelset one is more intense than that of wheelset three and the inner wheel wear is more intense than that of the outer wheel. The present analysis contributes to improve the life of tram wheel and rail and reduce the frequency of wheel-rail maintenance during operation.

Prediction of rail wear band evolution under excitation by periodic track irregularities and its influence on vehicle dynamic performance

Periodic track irregularities (PTIs) are an important and sensitive source of vehicle vibration and have attracted considerable attention from railway researchers. However, previous studies have mainly focused on the irregularities themselves, with limited investigation of how the rail wear band (RWB) generated by PTIs excitation affects vehicle responses. To assess this, a three-dimensional rail wear prediction model is developed in this study to simulate the evolution of the RWB under PTIs conditions and then the effect of RWB excitation on the vehicle dynamic performance is investigated. The results show that the spatial trace of the rail contact points generated by the PTIs also exhibits clear periodic characteristics. Accordingly, the distribution pattern of the RWB also reflects these periodic features. The excitation of the periodic rail wear band (PRWB) has a negative effect on the vehicle running stability, especially when the vehicle is traveling at speeds that induce coupling resonance, which exacerbates the consequences. As the wear level of the PRWB increases, there is also an escalation in the vibration of the carbody. Furthermore, the amplitude of the change in the wheelset balance position resulting from variations in rail profile is found to serve as an effective descriptor for characterizing the wear intensity of PRWB, and the vibration amplitude of the carbody exhibits a clear linear correlation with PRWB excitation. The research results are helpful for understanding the relationship between PRWB excitation and vehicle vibration behavior and serve as a theoretical basis for track maintenance and profile optimization.

Fault Diagnosis for Rail Profile Data Using Refined Dispersion Entropy and Dependence Measurements

Fault Diagnosis for Rail Profile Data Using Refined Dispersion Entropy and Dependence Measurements

Utilizing on-board sensing of passing train vehicles for virtual sensing of bridges

This study proposes a bridge response reconstruction approach using exclusively measurements obtained from instrumented train vehicles with known properties. The approach relies on an Augmented Kalman Filter (AKF) technique to estimate contact forces without prior knowledge of rail profile irregularities. Furthermore, the proposed methodology addresses the challenge of accurate vehicle-bridge interaction (VBI) contact force estimation in the presence of rigid-body modes in the train model, by separating rigid-body from flexible modes, and establishing a state-space formulation that incorporates the vehicle dynamics of solely flexible modes. The study examines, by means of numerical simulations, the impact of measurement and model errors, vehicle speed, and rail irregularities on both contact force estimation and bridge response reconstruction. It also investigates sensor configurations that minimize the number of sensors required on the train and shows that acceleration at all wheels and strain of at least one primary suspension per bogie are necessary for accurate force estimation. The results emphasize the significant role of VBI in achieving accurate bridge response reconstruction. The estimated contact forces in combination with a bridge model allow the reconstruction of the bridge response through virtual sensing, providing valuable information on the bridge’s health status.

Application of Online Automated Segmentation and Evaluation Method in Anomaly Detection at Rail Profile Based on Pattern Matching and Complex Networks

Application of Online Automated Segmentation and Evaluation Method in Anomaly Detection at Rail Profile Based on Pattern Matching and Complex Networks

Investigation of the effect of residual waviness formed by rail profile milling of reshaped surface on wheel-rail contact stresses and low cyclic fatigue

The rail profile milling process allows considerable thickness to be removed in a single pass. However, the residual waviness formation law after milling and its subsequent effect on rail performance remains uncertain. In this paper, the wavelength and wave height of the residual waviness are identified as the key characteristics. The relationship between milling speed and residual waviness is determined, and a numerical model of the residual waviness formed on the surface after milling is established based on the commonly used three milling speeds of 300, 600, and 1000 m/h, and its accuracy is verified using the parameters of the residual waviness detected by the milling experiments. A three-dimensional finite element analysis model of wheel-rail contact was employed to analyze the contact stresses and low fatigue cycles at a wheel load of 11.5 tons with no residual waviness on the rail profile surface and with three types of residual waviness, respectively. The results show that the residual waviness changes the wheel-rail contact position and the morphology of the contact area, reduces the maximum contact stress and increases the strain fatigue cycles. The application of elevated milling speeds reduces the wheel-rail contact stresses after milling, thereby increasing the low fatigue cycles.

Predicting the wear on the non-working side of the rail profile registration method and its validation

Abstract Rail cross-section profile detection can assess the wear and tear of measured rails, providing crucial references for railway maintenance and upkeep. It is challenging to use the conventional method to register only the profile of rail head detection accurately. After the rail edge adjustment in some conventional railways, it becomes difficult to determine the base point of rail profile registration. Due to the wear of non-working edge of rail, a rail profile registration method is proposed. Firstly, a wear prediction model based on the generalized regression neural network is constructed by optimizing smoothing parameters through ten-fold cross-validation to achieve the optimal values. This model predicts the wear values on the non-working rail edge, providing reliable coordinates for two wear points as alignment reference points. Secondly, an initial alignment between the measured profile and the target profile is achieved using the nearest point iterative algorithm, which ensures that both profiles are in the same region and oriented similarly. Thirdly, the weight is assigned to the wear measurement points based on their respective wear values. The predicted positions of wear points are applied for calculating the translation and rotation parameters. These parameters could align the measured profile and facilitate the final profile alignment. Lastly, the experimental profiles under rail adjustment conditions were registered, verifying the accuracy of the proposed method. The research results indicate that under rail adjustment conditions, the mean squared error (MSE) between the calculated and actual values of lateral wear is 0.094 mm2, which is lower than the MSE from manual measurements. The calculated lateral wear value for experimental profiles achieved high alignment and calculation accuracy. This method can be applied in practical projects, providing an effective solution for rail head profile alignment and serving as a reference for profile alignment on the non-working rail edge with incomplete measurement data.

Design of an Innovative Twin-Disc Device for the Evaluation of Wheel and Rail Profile Wear

Design of an Innovative Twin-Disc Device for the Evaluation of Wheel and Rail Profile Wear

Динамічні якості пасажирського поїзда зчленованої конструкції

The importance of this work stems from the need for renewing the home rolling stock and integrating it into the European market of transport services. The introduction of speedy train operation calls for increased attention to theoretical studies aimed at ride performance improvement. The analysis of passenger traffic safety for home and European tracks of different and geometries calls for assessing the ride performance of passenger trains. In doing so, steady and transient traffic conditions must be considered. This approach to theoretical studies allows one to improve the safety indices of speedy passenger trains. This paper presents the results of theoretical evaluation of the ride performance of an articulated and a standard passenger train. The cars of both trains are equipped with wheels of different profiles used on the Ukrainian and European railways. An assessment of the predicted dynamic performance of the articulated passenger train shows that its cars demonstrate a better ride performance, thus improving passenger comfort and train operating safety throughout the allowable speed range in normal conditions. It is undesirable for such trains to move in curves of small and medium radius. In addition, the articulated train cost is lower than the traditional train one because of a lower number of trucks. The ride performance is also assessed for cars whose wheels are compatible with different rail profiles, which allows them to be used both on the Ukrainian and on the European railways. As shown by calculations, the universal wear-resistant wheel profile does not impair the car ride performance of the trains considered. better ride performance.

Vibration-Based Railway Suspension Monitoring Under Varying Operating Conditions via Robust Statistical Time Series Type Methods

The on-board condition monitoring of railway suspension components is important for ensuring passenger comfort, maintaining proper operation, enhancing safety, and reducing operational costs. In this context statistical time series type random-vibration-based methods constitute a potentially efficient and cost-effective framework. Yet a significant challenge emerges from the competing requirements for high sensitivity to suspension component degradation and robustness to varying Operating Conditions (OCs) and uncertainty. Varying Operating Conditions and uncertainty are related to factors such as varying payload, travelling speed, and rail profile. The problem is further aggravated by the complexity of the vehicle dynamics, including the wheelbase filtering effect. Despite the significant progress made so far and the development of various methods, the challenges related to varying OCs and uncertainty largely remain as a technical barrier. The aim of the present study is the introduction of a data-based methodology aspiring to overcome them. The methodology is based on Multiple-Input Single-Output Transmittance Function (MISO-TF) time series models of a special form allowing for eliminating the effects of varying rail profile while properly treating the wheelbase filtering effect. Monitoring is based on a MISO-TF feature space, using either a Random Coefficient Gaussian Mixture or a Multiple Model based scheme. The former, although more elaborate, allows for the introduction of a complete probabilistic framework. An interesting advantage of both versions is that operation can be based on a very limited number of sensors and only a partial data-driven model of the dynamics. The methodology is comprehensively assessed via hundreds of SIMPACK based Monte Carlo runs under variable payload and rail profiles. The achievable performance is shown to be very good for both versions and for 10%, 15%, and 20% of condition degradation in either primary or secondary suspension components.

Carbody hunting behaviour of high speed vehicles in low effective conicity of wheel-rail contact

In the past, bogie hunting instability garnered more attention due to its potential for causing serious safety issues. However, with the rise in high-speed vehicle speeds and operational mileage, the concerns arising from carbody hunting can no longer be overlooked. This article investigates the causes and solutions for low-frequency carbody swaying under low effective conicity in wheel-rail contact, employing a combination of experimental and numerical techniques. Initially, the study involves an in-depth analysis of time-domain signals from lateral and vertical accelerations of the carbody and bogie frame during a high-speed train field-test. The carbody vibration mode resulting from carbody hunting displays a swaying motion at a frequency of 1.5 Hz. Subsequently, a dynamic system model of the high-speed vehicle was established and the suspension mode of the model were verified. The frequency of the carbody upper sway motion is determined to be 1.39 Hz. Low effective conicity is induced when grinding wheel match with new rail or new wheel match with grinding rail. Under this low effective conicity, the hunting frequency of the vehicle is around 1.5 Hz, close to the frequency of the carbody upper sway motion. The coincidence of the hunting frequency and the upper sway motion frequency leads to the low-frequency swaying of the carbody. At last, the effects of wheel profiles, rail profiles and suspension parameters on the carbody hunting have been studied. The study demonstrates that addressing low-frequency carbody swaying can be achieved through targeted rail grinding. This process involves modifying the rail profile while ensuring careful grinding of the rail inner corner. Furthermore, it has been confirmed in the article that replacing suitable yaw dampers is also effective.

Rail profile optimization of a subway’s sharp curve considering multiple curve sections and metal removal

Rail profile optimization of a subway’s sharp curve considering multiple curve sections and metal removal

Error correction method based on dual-beam laser for curved rail profile measurement

The current method of dynamic rail profile measurement involves the installation of a line-structured light sensor at the base of the train. The accuracy of this measurement is influenced by the vertical relationship between the laser plane of the light sensor and the longitudinal direction of the rail (LDR). When a train travels in a straight line, the normal of the laser plane aligns with the LDR. However, when the train curves, the angle at which its wheels connect with the rails causes the laser plane’s normal direction to deviate from the LDR, leading to measurement errors. To address this issue, we propose a method for curved rail profile measurement using a dual-beam laser to correct these errors. This method involves generating an auxiliary 3D rail reflecting the LDR and a virtual 3D rail reflecting the normal direction of the laser plane from the cross-section image of the dual-beam laser. An optimization function is then formulated to determine the optimal auxiliary plane (optimal-AP) by analyzing the alignment or intersection between the auxiliary and virtual 3D rails. Distorted contour points are projected onto the optimal-AP to rectify errors. Experiments validate the accuracy and effectiveness of this proposed method. The results show that, regardless of pitch or yaw movement between the laser plane and the LDR, the error in measuring corrected profile wear remains consistently below 0.10 millimeters, thereby meeting the accuracy standard for rail wear measurement. This approach rectifies measurement errors in curved rail profiles from a 3D perspective, ensuring accurate measurements even under complex working conditions. It also provides a valuable reference for error analysis and improving dynamic rail profile measurement accuracy.

Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method

AbstractThe diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning.

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between -1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

Dynamic rail wear measurement: integration of RTK GNSS, IMU, and laser

Currently, laser sensors are widely utilized in railway systems for monitoring rail wear. However, the lack or insufficiency of rail waist data poses a challenge to accurately match profiles and calculate wear. In this paper, we propose a novel approach for dynamic rail wear monitoring that comprises three major modules: a filtering methodology to smooth rail profile data, fine compensation to calibrate the angle distortion, and fast profile alignment. Initially, the AF technique and Kalman filter are applied to reconstruct the profile. This is followed by integrating real-time kinematic global navigation satellite system, inertial measurement unit, and laser to precisely correct the rail profile dynamic distortion. Additionally, genetic algorithms-least squares method is used for rapid determination of the R20mm center of the profile. Rigorous testing and trials have demonstrated that our proposed approach achieves precision as low as 0.03 mm with significant potential in dynamic rail wear monitoring.