Axle Box Acceleration Research Articles

LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation

Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision or axle box acceleration face challenges such as high costs, complex equipment installation and maintenance, as well as difficulties in achieving real-time performance. To address these challenges, this study proposes an innovative low-cost real-time recognition network (LCRTR-Net), which utilizes accelerometers installed on the underside of the train body and combines wavelet packet decomposition with dilated causal convolution in a residual neural network. Specifically, the approach first extracts the latent features of train body acceleration caused by rail corrugation through wavelet packet decomposition and reconstruction. Next, dilated causal convolution is employed to capture the temporal causal relationships and long-term dependencies of these latent features. Finally, the integration of residual connections further enhances the feature extraction performance and computational efficiency of LCRTR-Net. Experimental results demonstrate that LCRTR-Net exhibits significant generalization ability and real-time performance, achieving an average recognition accuracy exceeding 97.0%, with a recognition time of only 0.17 ms per rail corrugation sample, significantly outperforming existing rail corrugation recognition methods. This indicates that LCRTR-Net has broad application potential in practical railway operations. Future research directions will focus on unsupervised or few-shot learning algorithms and multi-sensor integration to further improve recognition accuracy and real-time performance, promoting the practical application of this technology.

Compilation of wheel-rail comprehensive irregularity spectrum for subway vehicle

The irregularity excitation experienced by subway vehicles is mainly the result of the interaction between the track and wheel. However, in the early system design and simulation analysis of subway vehicles, most only used the traditional standard track irregularity spectrum as the input excitation, ignoring or underestimating the contribution of the wheel irregularity. Based on our statistical analysis of 200,000 kilometers of tracking test data of subway vehicle wheel irregularities, we found that the short-wave irregularity caused by the wheels far exceeds the traditional standard track irregularity. The service condition of the vehicle is seriously affected, especially in the final stage of a wheel re-profile period. To address the above issues: Firstly, the sensitive wavelength range (16. 67∼2500 mm) of subway vehicles was derived based on the axle box acceleration spectrum of IEC61373: 2010, which was very close to the wavelength range (50∼2627 mm) of the wheel irregularity spectrum proposed later, demonstrating the importance of compiling a wheel irregularity spectrum; Secondly, based on the large number of tracking test data of wheel out-of-roundness, a calculation method of the wheel irregularity quantile spectrum under the Johnson non-normal transformation system was proposed; Thirdly, according to the different stages of the wheel re-profile period, the wheel irregularity spectrum is introduced to correct the short-wave segments of the traditional standard track irregularity spectrum to compile a wheel-rail comprehensive irregularity spectrum.

Enhanced vertical railway track quality index with dynamic responses from moving trains

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m-1 to 0.33 m-1 (substructure) and 1.25 m-1 to 2.50 m-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

Modelling of high frequency vibration of railway bogies’ subcomponent based on structural dynamics: A case study for lifeguard of metro bogie

The sub-components of railway bogie have been frequently reported to be subjected to the vibration fatigue due to the wheel/rail high frequency vibration. An efficiency numerical model is thus desirable to simulate the high frequency vibration of railway bogie, which can substantially enhance the design efficiency of railway bogie system considering the vibration fatigue. Therefore, this paper aims to proposing a modelling methodology for simulating the high frequency vibration of railway bogies’ subcomponents, and a lifeguard of metro bogie was taken as an example. Firstly, a field test measurement of lifeguard for the metro bogie was primarily introduced to demonstrate the high frequency vibration characteristics and the related failure mechanisms arising from the wheel/rail high frequency impact. Subsequently, a random vibration model for the railway bogie system and the lifeguard based on the rigid/flexible coupled dynamics were developed to simulate the high frequency vibration and the dynamic stress developed at the lifeguard. This method was subsequently employed in the structural optimization for the lifeguard. The results showed that the proposed methodology can effectively simulate the high frequency vibration of lifeguard of bogie system based on the measured axle box acceleration, and the optimized structure can effectively increase the service lifetime under the excitation of wheel/rail high frequency vibration.

A novel optimal resonance band selection method for wheelset-bearing fault diagnosis based on tunable-Q wavelet transform

The detection of faults in the wheelset-bearing system is crucial for guaranteeing the safety of train operations. The core is to extract the optimal resonance band (ORB) and repetitive transient impact signals from the collected axle box acceleration signals. Accordingly, a novel automatic fault detection method called the bidirectional iterative merging multi-Q tunable-Q wavelet transform (BIMMQTQWT) is proposed to address the issue that existing methods are vulnerable to background noise and irrelevant components. First, a series of band-pass filters with almost constant bandwidth are constructed by the improved multi-Q tunable-Q wavelet transform (IMQTQWT) derived from the fault characteristics. Second, the fault information contained in each sub-band coefficient is preliminarily estimated using the correlative envelope comprehensive indicator (CECI). Third, the ORBs are automatically selected using the maximum CECI based on a strategy called bidirectionally merging adjacent frequency bands (BIMFBs). Finally, Envelope demodulation based on the ORB is executed followed by identifying bearing faults. The effectiveness in detecting multiple wheelset-bearing faults of the proposed method is validated through simulation and bench experiment signals. And the superior performance of the proposed method is exhibited compared with the existing average infogram and resonance sparse decomposition.

Detection of ballastless track interlayer gap based on vehicle’s multivariate dynamic response and deep learning

To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data of vehicles under the edge type and internal type interlayer gaps are analyzed. An improved ensemble local mean decomposition algorithm (IELMD) is designed to achieve data denoising and preliminary enhancement preprocessing of weak damage features for VABA and VWRF under interlayer gaps. The measured and simulated VABA and VWRF constituted multiple dynamic response data sets for interlayer gap detection. An interlayer gap detection model: DTA-Tcnformer is constructed, integrating a dual temporal convolutional network with an attention mechanism, transformer architecture, and gate control mechanism. Multiple dynamic response data are input into the model to locate and classify 24 cases of the two interlayer gap types. The influence of damage cases and vehicle speeds on the location effect is analyzed. The learning ability of the proposed model on the gap features is visualized. The misidentification and omission of the gap cases are calculated, and the comprehensive recognition accuracy is 92.48 %. The recognition performance of the proposed model is compared and verified.

Mechanism-driven improved SVMD: an indirect approach for rail corrugation detection using axle box acceleration

Abstract Addressing the challenge of the current inability to qualitatively identify rail corrugation damage accurately from axle box acceleration data, this study proposes a novel approach. To indirectly identify rail corrugation from axle box acceleration, we introduce an improved successive variational mode decomposition (SVMD) algorithm, coupled with a deep learning model for corrugation recognition. First, a numerical model of the vehicle-rail-track slab system is established, considering rail corrugation. Indicator analysis is integrated into the SVMD. The improved SVMD is employed to decompose and reconstruct axle box acceleration, achieving noise reduction and extraction of useful components. Next, we apply the fast Continuous Wavelet Transform analysis method to effectively transform one-dimensional data into two-dimensional images. Finally, the You Only Look Once (YOLO) model serves as a classifier for the classification and recognition of corrugation with different wavelengths. The results demonstrate that the mechanism-driven improved SVMD effectively extracts corrugation components from axle box acceleration, while the YOLO model achieves rapid and efficient identification and classification of corrugation with different wavelengths. The results show that compared with other traditional models, the training time of the YOLO model is 60%–90% of the training time of the traditional algorithm, and the Recall rate is 1.15–1.45 times that of the traditional algorithm. In terms of wavelength identification, the YOLO model has a recognition rate of 98% for different wavelengths. The proposed approach offers an innovative and efficient solution for identifying rail corrugation damage in axle box acceleration data.

Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage

The following applications of machine learning/AI for railway vibration will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, for tunnel lines (partially) physics-based ML for surface lines). 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, ML for layered soil). 3. Track damage detection and quantification from FRF and moving load response. 4. Bridge damage detection and localisation from modal analysis and moving load response. 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. Prediction calculation of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software, fast calculations are needed. Several time-consuming detailed calculations should be used to develop simpler and fast models. The dynamic stiffness of isolated or un-isolated railway tracks from detailed calculations with a continuous soil is approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction ) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance.

Development of fatigue prediction system for bogie frame using a dynamic analysis model based on high‐speed and high‐precision stress estimation method

AbstractDespite ensuring the integrity of bogie frames for railway vehicles via nondestructive inspections during periodic maintenance, the possibility of fatigue cracks occurring at locations other than the predetermined inspection points cannot be dismissed. Therefore, fatigue cracks can be prevented more efficiently by assessing the overall degree of fatigue damage to the entire bogie frame and determining the results via nondestructive inspections. In this study, dynamic stresses in the bogie frame during running were estimated via finite element dynamic analysis by using the axle box acceleration as input, and the degree of fatigue damage and life were calculated from the waveform of the estimated stresses. Furthermore, we developed a bogie frame fatigue prediction system based on a high‐speed and high‐precision stress calculation method. The developed system visualized the overall relative life.

Wheel flat detection by using the angular domain synchronous averaging method and axle box acceleration: Simulation and experiment

Wheel flats (WFs) can generate periodic impact forces and aggravate the wheel-rail interaction. Therefore, the early detection of WFs is significant for railway operators to lower the maintenance cost. In this work, the angular domain synchronous averaging (ADSA) method is employed to detect WFs on railway vehicles since it can handle the non-stationary vibration process of rotating machines by processing the vibration data in the angular domain. Furthermore, the short-time irregular impulses, such as those caused by crossings and rail joints, can be attenuated by using synchronous averaging technology. The periodic impact caused by WFs can be easily observed in the polar coordinate using the ADSA method, which is verified by one multibody system (MBS) simulation and one field experiment. Besides, the sensitivity of this method to various flat dimensions and multiple flats is also studied in the MBS simulation.

Detection of Rail Fastener Assembly Defects Using Axle-Box Acceleration

Detection of Rail Fastener Assembly Defects Using Axle-Box Acceleration

Vertical dynamic measurements of a railway transition zone: a case study in Sweden

AbstractThis study presents a measuring framework for railway transition zones using a case study on the Swedish line between Boden and Murjek. The final goal is to better understand the vertical dynamics of transition zones using hammer tests, falling weight measurements, and axle box acceleration (ABA) measurements. Frequency response functions (FRFs) from hammer tests indicate two track resonances, for which the FRF magnitudes on the plain track are at least 30% lower than those at the abutment. The falling weight measurements indicate that the track on the bridge has a much higher deflection than the track on the embankment. Two features from ABA signals, the dominant spatial frequency and the scale average wavelet power, show variation along the transition zone. These variations indicate differences in track conditions per location. Finally, the ABA features in the range of 1.05–2.86 m−1 are found to be related to the track resonance in the range of 30–60 Hz. The findings in this paper provide additional support for physically interpreting train-borne measurements for monitoring transition zones.

In-situ assessment of the sound and vibration reduction performance of a new type of resilient wheel installed on a metro train

Based on a new type of resilient wheel installed on a linear motor subway train, a measurement campaign is devised to evaluate the effectiveness of the resilient wheel in reducing vibration and noise. For reference, the same series of tests are carried out on another train of the same type but fitted with conventional wheels. The measurements consist of the vibration and noise of various parts of the vehicle-track system which are used to investigate the sources of wheel-rail rolling noise, the acceleration of the axle boxes and the rail head, the noise inside the vehicle, and the noise close to the wheels. It is found that the interior noise levels in the driver’s cab and passenger car even when running on a curve with corrugated rails drop below the required limits when using the new type of resilient wheels. The axle box acceleration is used as an indication of the effectiveness of the resilient wheel in reducing the vibration to guarantee the reliability of the bogie frame. The vibration in the lateral direction is reduced considerably on both straight and curved tracks, while in the curve it has a greater decrease.

A quantitative detection method for wheel polygonization of heavy-haul locomotives based on a hybrid deep learning model

Wheel polygonization poses severe threat to the operational safety of railway vehicles. To improve running safety, condition monitoring of wheel polygonization based on axle box acceleration (ABA) signals is an effective method. However, the signals usually contain strong noise, making it difficult to diagnose polygonal wheels quantitatively. To address these issues, this paper presents a quantitative detection method for wheel polygonization based on a deep learning (DL) model that integrates Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM). The CNN is responsible for automatically extracting the sensitive spatial features from the original input and it can retain effective signal components. The LSTM is introduced to fuse the local features and conduct sequence modeling for polygonal wear amplitude estimation. Both dynamics simulations and field tests are carried out to verify the feasibility of the proposed method. The detection results indicate that the proposed model can accurately estimate the wear amplitudes.

Research on fatigue evaluation method of high-speed train axle based on axle box acceleration

As a key safety component of the high-speed train, fatigue fracture of the axle would lead to major accidents such as derailment or overturning. The complexity of the axle dynamic stress test seriously enhances the difficulty of axle fatigue damage analysis. In this paper, the dynamic stress test of the high-speed train axle was carried out, the axle box acceleration was monitored on-track during the test, and the relationship between the axle stress spectrum and acceleration was analyzed on-track. The results show that the relationships between the axle equivalent stresses and the Root Mean Square (RMS) values of the axle box vertical acceleration and lateral acceleration exhibit a strong joint probability density distribution. The concept of the virtual surface density of wheel-rail contact is also proposed to realize the purpose of using axle box acceleration to deduce axle equivalent force. The results quantify the relationship between axle box acceleration and axle equivalent force, provide a new method for predicting the axle damage using the acceleration RMS values, and open up a new approach for structural health monitoring of high-speed train axles.

An approach for the estimation of lateral track irregularity using axle-box acceleration

PurposeThis article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.Design/methodology/approachFirstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.FindingsBy comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.Originality/valueThis article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.

Analysis of Axial Acceleration for the Detection of Rail Squats in High-Speed Railways

A squat is a type of fatigue defect caused by short-wavelength rotational contact; if squats are detected early, the maintenance cost of the track can be effectively reduced. In this paper, a method for the early detection of squats is presented based on ABA (axle box acceleration) and frequency signal processing techniques. To increase the measurement sensitivity for the squat, ABA was used to measure the longitudinal vibration. Compared to vertical ABA, longitudinal ABA does not include vibrations from rail fasteners and sleepers, so it is possible to effectively measure the vibration signal in relation to the impact of the rail. In this paper, vibration data were measured and analyzed by installing a 3-axis accelerometer on the wheel axle of the KTX; squat signals were more effectively extracted using the longitudinal vibration measurement presented above. The algorithm to detect the position of squats was developed based on wavelet spectrum analysis. This study was verified for the section of a domestic high-speed line, and as a result of conducting field verification for this section, squats were 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 unsupported sleepers occurred at locations where the squats occurred in some sections.

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.

The Dynamic Train–Track Interaction on a Bridge and in a Tunnel Compared with the Simultaneous Vehicle, Track and Ground Vibration Measurements on a Surface Line

The vehicle–track interaction generates forces and consequently vibrations in the environment. The interaction has been analysed by the simultaneous measurements of vehicle, track and ground vibrations during test runs with varied train speeds. The special effects of the passage over a bridge and through a tunnel are studied and compared with the measurements on a conventional ballasted surface line. The maximum amplitudes, narrow band and one-third octave band spectra are presented for the axle-box accelerations and for the track, bridge and ground vibrations. The different frequencies and frequency bands are related to wheel out-of-roundness, track alignment errors, the sleeper passage and the wheelset–track resonance. An axle impulse component has been observed at the track, at the near-field soil and as a scattered version in the far field. Specific results can be found for the bridge track, where clearly speed-dependent bridge resonances occur due to the axle sequence of the train, and for the tunnel track where soft rail pads are responsible for a strong amplification around the wheelset–track resonance. On the other hand, the axle impulses are strongly reduced by the tunnel track, and the scattered axle impulse component is not as relevant as for the surface track. As a consequence, a strong mid-frequency amplitude reduction of the tunnel compared to the surface line has been measured for low and high train speeds by the Federal Institute of Material Research and Testing (BAM) and by other institutes.

Vold–Kalman filter order tracking of axle box accelerations for track stiffness assessment

Intelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of railway infrastructure in the face of increasing demands on cost and operations efficiency. Numerous studies have highlighted track stiffness as a main parameter influencing the evolution of degradation that drives maintenance processes. As such, the measurement of track stiffness is fundamental for characterizing the performance of the track in terms of deterioration rate and noise emission. In this work, we propose a rail stiffness assessment scheme relying on low-cost, on board monitoring sensors, namely axle-box accelerometers, that are mounted on in-service trains and enable frequent, real-time monitoring of the railway infrastructure network. A Vold–Kalman filter is proposed for decomposing the signal into periodic wheel and track related excitation–response pair functions. We demonstrate that these components are in turn correlated to operational conditions, such as wheel out-of-roundness and the rail type. We further illustrate the relationship between the track stiffness, the measured wheel-rail forces and the sleeper passage amplitude, which can ultimately serve as an indicator for predictive track maintenance and prediction of track durability.