Axle Box Acceleration Research Articles

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 Wasserstein generative adversarial network-based approach for real-time track irregularity estimation using vehicle dynamic responses

Accurate and timely estimation of track irregularities is the foundation for predictive maintenance and high-fidelity dynamics simulation of the railway system. Therefore, it’s of great interest to devise a real-time track irregularity estimation method based on dynamic responses of the in-service train. In this paper, a Wasserstein generative adversarial network (WGAN)-based framework is developed to estimate the track irregularities using the vehicle’s axle box acceleration (ABA) signal. The proposed WGAN is composed of a generator architected by an encoder-decoder structure and a spectral normalised (SN) critic network. The generator is supposed to capture the correlation between ABA signal and track irregularities, and then estimate the irregularities with the measured ABA signal as input; while the critic is supposed to instruct the generator’s training by optimising the calculated Wasserstein distance. We combine supervised learning and adversarial learning in the network training process, where the estimation loss and adversarial loss are jointly optimised. Optimising the estimation loss is anticipated to estimate the long-wave track irregularities while optimising the adversarial loss accounts for the short-wave track irregularities. Two numerical cases, namely vertical and spatial vehicle-track coupled dynamics simulation, are implemented to validate the accuracy and reliability of the proposed method.

An unsupervised method based on convolutional variational auto-encoder and anomaly detection algorithms for light rail squat localization

As a typical short-wave rolling contact fatigue defect of rail surface, squats accelerate the degradation of the railway infrastructure. Early detection of rail squats contributes greatly to the maintenance of the railway system. This article proposes an automatic detection algorithm for light rail squat localization via the vehicle axle box acceleration signal. A convolutional variational auto-encoder, named CVAE, is tailored for feature extraction in an unsupervised manner. Then, two anomaly detection methods, namely elliptic envelope and one-class support vector machine (OCSVM) are employed as alternatives for rail squat detection. The validity and reliability of the proposed approach are verified by vehicle-track coupled dynamics simulation, where squats are considered as an irregularity excitation. And an in-lab experiment is conducted to further validate the effectiveness of the method. Comparative studies are conducted to investigate the influence of squat depth, feature extraction method, noise level and vehicle speed on the algorithm. The results demonstrate the elliptic envelope method is a better option for squat detection which can be explained by the consistency of the property of features extracted by CVAE and input data requirements of the elliptic envelope method. Furthermore, compared to the convolutional auto-encoder, CVAE is a better unsupervised feature extraction method because features extracted by CVAE are more sensitive to light squats. And it’s verified that the CVAE-elliptic envelope method is robust over signal noise and variability of vehicle speed.

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.

Study of Rail Squat Characteristics through Analysis of Train Axle Box Acceleration Frequency

In this study, a method for detecting the railway surface defects called “squats” using the ABA (Axle Box Acceleration) measurement of trains was proposed. ABA prototype design, implementation, and field tests were conducted to derive and verify the results. The field test was performed using a proven precision measurement system, and the measured data were signal-processed using a Matlab program. The algorithm used to determine the position of the squats was developed based on wavelet spectrum analysis. This study was verified for a section of a domestic general line and, following field verification for the section, squats was detected with a hit rate of about 88.2%. The main locations where the squats occurred were the rail welds and the joint section, and it was confirmed that in some sections, unsupported sleepers occurred at the locations where the squats occurred.

Effects of wheelset flexibility on locomotive–track interaction due to rail weld irregularities

Irregularities of rail joints ordinarily induce high-frequency impacts on wheel–rail contact systems, which may further influence vehicle–track interaction. In this study, a three-dimensional locomotive-track coupled dynamics model that uses the wheelset flexibility was developed. The wheelset rigid motion and elastic deformation are calculated based on the multi-body dynamics theory and finite element method, respectively. The effectiveness of this model was validated. The effect of wheelset flexibility on locomotive–track interaction due to rail weld irregularities is analysed by comparing the dynamic responses obtained using the rigid model and the proposed rigid–flexible coupled model. The proposed model is applied to the sensitivity analysis of the wheelset response to the rail weld geometry irregularity. The results show that the dominant frequency of the wheel–rail force or axle–box acceleration is 81 Hz, which is the 1st bending modal frequency of the wheelset. The P2 resonance frequency is easily excited owing to impacts of rail weld irregularities, which may induce the formation of locomotive wheel polygonization. The wheelset acceleration was more sensitive to the wavelength than the depth of the rail weld irregularities. The wavelength characteristics can be significant in vehicle vibration-based condition monitoring of rail weld irregularities.

Machine learning aided rail corrugation monitoring for railway track maintenance

Urban rail transit is a critical infrastructure system that supports urban economic and social development. It has a significant mass transportation capacity while enables environmental benefits. Public transport is a way to resolve large-scale urban road traffic problems and contributes towards sustainable development. However, with the operations of railway vehicles on curves, unbalanced and undulated wears often appear on rails, especially on the low rail. This rail surface defect, so-called 'rail corrugation', directly affects the service life of rolling stocks and track components. The high-frequency vibrations caused by train-track interaction over rail corrugations also impair passenger ride comfort and generate excessive noises. In severe cases, the defects may even endanger the safe passage of a railway vehicle. In practice, rail corrugation has brought huge challenges to the reliable operations and maintenance of railway networks. With the continuous expansion of railway lines and the increasing traffic demands, any existing rail corrugation test method is not enough to meet the actual needs of track maintainers to promptly identify and mitigate rail surface defects. Therefore, this investigation aims to establish a new technique to prognose and classify rail corrugations efficiently and effectively. This study adopts D-track dynamic simulation package to obtain over thousands of vibration data in the form of axle box accelerations from train-track interactions under different conditions. Neural network models have been developed to recognize the rail corrugations and then classify their severity to aid the planning and prioritization of rail track maintenance activities. The models have been trained and tested using the vibration data, achieving the accuracy of over 90%. The optimal model has then been highlighted. The investigation has demonstrated the potential of the neural network to detect and classify rail corrugations, which can be used practically for curved track condition monitoring and maintenance planning.

Vehicle running instability detection algorithm (VRIDA): A signal based onboard diagnostic method for detecting hunting instability of rail vehicles

In recent years, significant research transpired on onboard monitoring of various phenomena arising in dynamic vehicle-track interaction. One key issue being monitoring of vehicle hunting instability. Current hunting detection standards are appropriate for certification tests of vehicles, but incapable to monitor the health of the vehicle and track subsystems influencing the hunting instability. This paper proposes a signal based procedure for accurately triggering Hunting/No-Hunting alarm by conforming to requirements of onboard monitoring. A new method is conceived to reveal coherence among lateral and longitudinal accelerations during vehicle hunting. Furthermore, an index which amalgamates phase and amplitude information of lateral and longitudinal axlebox accelerations is introduced to detect coupled modes in lateral and yaw directions, i.e. hunting modes. Several simulations based pragmatic case studies are performed to assess the efficacy of the proposed procedure. The proposed method outperforms traditional hunting detection procedures by detecting more Hunting/No-Hunting occurrences. The proposed method contributes towards digitalization of rail vehicles through condition-based and predictive maintenance.

On the genesis of squat-type defects on rails – Toward a unified explanation

Different origination theories for squat-type rail defects are examined and confronted with experimental evidence. Based on the morphology of the three-dimensional defect crack pattern, with a leading crack directivity governed by the tangential stress history, it is shown that theories assuming dynamic wheel-rail interaction as a direct initiation mechanism of the double-lobed defect violate the causality principle. Rail surface anomalies of three categories increase the tangential stress exposure and thus the risk of defect development: pertaining to the material properties, contact geometry (including both the global scale, involving the dynamic stress level transmitted by the contact area, and the local scale, involving transient stress redistribution within this area), and contact stiffness. Both detection measures focusing on geometrical surface deviations giving rise to dynamic wheel-rail interaction (such as axle-box acceleration measurements) and the idea of a critical diameter for such imperfections are inadequate. Instead, and apart from the surface geometry, steel micro-cleanliness and chemical composition, phase transformation mechanisms of surface material (due to operational conditions or treatment by grinding or milling) and welding deserve attention. Fluid entrapment remains a potentially contributing factor in early growth, while crack face oxidation and corresponding volumetric expansion may contribute at any stage after initiation.

Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Vibration-based damage detection of rail fastener using fully convolutional networks

The rail fastener plays an important role in supporting the rail and isolating the train-track system vibrations. Its damage detection is therefore an inevitable part in railway maintenance to ensure the system performance. Recently, deep learning techniques are widely applied to structural health monitoring, including damage detection of the rail fastener based on visions. However, the vision-based detection is mainly limited to visible damages. To address this limitation, this work presents a vibration-based detection method by introducing the fully convolutional network (FCN) to identify invisible damages of fasteners. Firstly, three damage categories are defined and five damage degrees are equivalently represented by modifying the stiffness and damping coefficients of target fasteners. Then, a vehicle-track vertically coupled dynamics model with variable vehicle speeds is established to obtain axle box accelerations (ABAs) under excitations of fastener damage and track irregularity. Finally, a fastener damage detection network is designed based on the FCN architecture to predict damage degrees by inputting the ABA, the track irregularity and the vehicle speed simultaneously. The detection performance is estimated and the network robustness to noise is analysed. The results show that the proposed method is capable of achieving accurate, real-time and robust identification of the fastener damage.

열차 축상가속도 주파수 분석을 통한 레일 스쿼트 특성 연구

열차 축상가속도 주파수 분석을 통한 레일 스쿼트 특성 연구

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

The dynamic resonance under multiple flexible wheelset-rail interactions and its influence on rail corrugation for high-speed railway

The wheel-rail dynamic resonance (W-RDR) under multi wheelset-rail nonlinear interactions in the mid-high frequency range is still unclear regarding wheelset flexibility, wave transmissions and reflections (W-TF) among each wheel-rail interaction. To clearly elucidate W-RDR and its induced modes, a double vehicle-track coupled dynamic model of multiple flexible wheelset-rail interactions is established. The validity of the model in the mid-high frequency range is verified by comparing the simulated axlebox acceleration with the measured data. Then, the W-RDR (vertical force, friction power) is calculated and analyzed. Furthermore, the W-RDR under the single/ double/ multiple wheelset-rail interactions is compared and the rail bending modes between bogie wheels are proposed. The influence of wheelset flexibility and W-TF among each wheel-rail interaction on W-RDR are clarified. Finally, the initiation mechanism of rail corrugation is revealed based on corrugation growth rate. The results show that the wheelset flexibility has limited influence on vertical force resonances but significant contribution to friction power resonances. The W-TF between bogie wheels produce three rail local bending modes. The W-TF between wheels of adjacent bogies further intensify response peaks but do not generate new resonances. The W-RDR is mainly caused by the P2 resonance mode, the rail bending modes between bogie wheels and the wheelset bending modes. The rail 3rd-order bending, pinned-pinned resonance between bogie wheels and the wheelset 2nd antisymmetric bending resonance are key factors in corrugation initiation. Moreover, the wheel-rail resonance at the bogie leading wheelset is more prone to produce corrugation than that at the rear wheelset.

Comparison of axle-box acceleration in a low frequency range under 30Hz during vehicle’s passing over rail-rupture parts and joints.

Comparison of axle-box acceleration in a low frequency range under 30Hz during vehicle’s passing over rail-rupture parts and joints.

Simple dynamic model of predicting the axle-box acceleration when trains pass through the rupture part of rails

Simple dynamic model of predicting the axle-box acceleration when trains pass through the rupture part of rails

Damage tolerance of fractured rails on continuous welded rail track for high-speed railways

Broken gap is an extremely dangerous state in the service of high-speed rails, and the violent wheel–rail impact forces will be intensified when a vehicle passes the gap at high speeds, which may cause a secondary fracture to rail and threaten the running safety of the vehicle. To recognize the damage tolerance of rail fracture length, the implicit–explicit sequential approach is adopted to simulate the wheel–rail high-frequency impact, which considers the factors such as the coupling effect between frictional contact and structural vibration, nonlinear material and real geometric profile. The results demonstrate that the plastic deformation and stress are distributed in crescent shape during the impact at the back rail end, increasing with the rail fracture length. The axle box acceleration in the frequency domain displays two characteristic modes with frequencies around 1,637 and 404 Hz. The limit of the rail fracture length is 60 mm for high-speed railway at a speed of 250 km/h.

A time domain method for wheel-rail force identification of rail vehicles

Using dynamic load identification technology to identify the wheel-rail force of rail vehicles is an economical and convenient method for indirect measurement. In this paper an analytical load identification method is proposed. Firstly, based on the theory of structural dynamics, the Duhamel integral is applied, combined with the characteristics of the rail vehicle system, to derive the analytical load identification method. Secondly, the correctness of the method is verified by a multi-degree-of-freedom system with exact solution. Next, a detailed vehicle dynamics simulation model is established as a virtual test vehicle and source of virtual measurements of the vehicle responses (axlebox accelerations). Different operating conditions are considered, and the virtual acceleration measurements are used as input to the load identification method for indirect measurement of the wheel-rail forces. Finally, the identified vertical and lateral wheelset force, vertical force of the left and right wheels, derailment coefficient and wheel unloading rate are compared with the corresponding time-histories from the virtual test-vehicle simulations. A good agreement is found with all of their correlation coefficients above 0.8. This result has important implications on the safety evaluation of vehicle curve negotiation by using acceleration measurements only, without the need for direct force measurements.

Influence of viscoelastic mechanical properties of rail pads on wheel and corrugated rail rolling contact at high speeds

The rubber tie-plates on rail fasteners display very marked nonlinear stiffness and damping characteristics, and play an important role in railway systems. This paper studies the influence of the viscoelastic mechanical properties of the rubber tie-plate on the transient rolling contact behavior of the wheel and rail. Firstly, the viscoelastic mechanical properties of high-speed rail pads were obtained through laboratory tests and described theoretically by means of a modified fractional derivative Zener model. After this, an explicit finite element model is proposed to investigate the wheel and corrugated rail rolling contact. The numerical study shows that the viscoelastic mechanical properties have a significant effect on the response strength of the wheel-rail force and axle box acceleration at the main frequencies.

Research on Hunting Instability Monitoring System of High Speed Freight Bogie

High speed freight trains have become a new trend in international train operation, and many countries have studied the high speed freight trains. Therefore, it is necessary to carry out relevant safety monitoring research on high speed freight trains. According to the existing acceleration alarm devices for high speed trains and normal speed trains, and combined with the reality condition of high speed freight train, an empty train hunting alarm device is proposed. Through the simulation analysis of the hunting wavelength and acceleration of freight car, it is proposed that the wave filtering frequency should be controlled at 0-15Hz, and the recommended value of lateral alarm limit of frame lateral acceleration is 0.6g. Finally, the suggestion of axle box acceleration installation is put forward, which is installed in the middle position above the axle box to evaluate the wheel rail state. It can mainly evaluate the common wheel rail damage such as wheel polygon, wheel flat and corrugation of rail.