Figure - available from: Applied Sciences
This content is subject to copyright.
Source publication
Railway tracks must be managed appropriately because their conditions significantly affect railway safety. Safety is ensured through inspections by track maintenance staff and maintenance based on measurements using dedicated track geometry cars. However, maintaining regional railway tracks using conventional methods is becoming difficult because o...
Similar publications
![Interruption of the traffic of the station switch [45].](publication/382477952/figure/fig3/AS:11431281371677637@1744384326639/Interruption-of-the-traffic-of-the-station-switch-45_Q320.jpg)
![Interruption of track on single-track and double-track lines [45].](publication/382477952/figure/fig4/AS:11431281371735541@1744384327036/Interruption-of-track-on-single-track-and-double-track-lines-45_Q320.jpg)
A planned interruption of railway infrastructure is a situation where the operation of the track line or the operation of railway transport is limited. If there is also a restriction on the railway infrastructure, it means there will be complications not only for passengers but, above all, for railway undertakings operating freight transport. Howev...
Citations
... Similarly, Zhang et al. [29] extracted four features in the time domain and eight features in the frequency domain from carbody acceleration to estimate track irregularities. Tsunashima et al. [30,31] used the time − frequency analysis and Kalman filter to analyze the vertical acceleration of the carbody. They pointed out that this method can effectively evaluate the vertical track irregularity and should be employed in practice. ...
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies. Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis. Driven by multi-source measurement data, including the axle box, the bogie frame and the carbody accelerations, this paper proposes a track irregularities monitoring network (TIMNet) based on deep learning methods. TIMNet uses the feature extraction capability of convolutional neural networks and the sequence mapping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities. The particle swarm optimization algorithm is used to optimize the network parameters, so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains. The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model. Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities. Furthermore, comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities (vertical R 2 of 0.91, lateral R 2 of 0.84 and time cost of 10 ms), compared to other classical regression. The test also proves that the TIMNet has a better anti-interference ability than other regression models.
... Some of the authors who use measurements from the train body attempt to estimate track geometry by using a dynamic model and different types of Kalman filters [18,26,[45][46][47][48][49]. Others extract signal features in time domain and in frequency domain and use them as TQI [25,[50][51][52] or to train ML models [22,28,30,53,54]. ...
... Thanks to this, they can adapt and model every interaction related to their management, and as a result, implement maintenance activities as part of the so-called "Data Driven Maintenance", i.e. maintenance based on data [58][59][60][61]. In the railway industry, such solutions are already being implemented -in the case of diagnostics of switch drives [62][63][64] and methods of monitoring the condition of tracks involving the installation of measuring devices, not on specialized rail cars, but on scheduled trains [65,66]. Only such models of real infrastructure objects can be treated as their digital twins, because they already have the ability to self-update. ...
The introduction of the article highlights the importance of measuring the wear of rails and turnouts. The evolution of methods and devices used to measure the profiles of these elements is briefly presented. The principle of conducting research using the LiDAR technique is explained. The problem of geometric and structural differences of tram tracks in relation to classic railways is pointed out, and the resulting concerns about the possibility of adapting typical railway methods of measuring rail and turnouts profiles to tram tracks. The rest of the article describes the construction, basic parameters and method of operation of a precise stationary laser scanner, dedicated to measuring rail profiles and turnouts. Graphical analysis of the results for measurements carried out with the mentioned device on tram tracks are presented – for rails in curves with small radii, turnouts (half-switches and frogs), corrugated wear, and broken welds. The summary presents conclusions from the research conducted.
... 14 (8) 16 (9) 19 (11) 23 (15) 25 (17) 27 (19) 30 (22) Twist 23 (18) Target value of track irregurality correction for riding comfort Target value of track irregurality correction for safety +10 (+6) -5 (-4) 20 (14) 25 (19) 20 (14) < l a t e x i t s h a 1 _ b a s e 6 4 = " 3 E A x G d D K Y G H 4 k 8 U z v b 8 p z P l a z e A = " > A A A C b H i c h V G 7 S g N B F D 1 Z X z E + E h + F I M J i U K z C X R E V q 6 C N p U b j A x P C 7 j r G x X 2 ...
... Therefore, it is necessary to examine the effect of track conditions on the car body vibration. Tsunashima et al. developed a system to identify track faults using accelerometers and a GNSS placed on the car bodies of in-service vehicles [15,16]. Bai et al. used low cost accelerometers placed on or attached to the floors of operating trains to analyse track conditions [17]. ...
Track and preventive maintenance are necessary for the safe and comfortable operation of railways. Track displacement measured by track inspection vehicles or trolleys has been primarily used for track management. Thus, vibration data measured in in-service vehicles have not been extensively used for track management. In this study, we propose a new technique for estimating track irregularities from measured car body vibration for track management. The correlation between track irregularity and car body vibration was analysed using a multibody dynamics simulation of travelling rail vehicles. Gaussian process regression (GPR) was applied to the track irregularity and car body vibration data obtained from the simulation, and a method was proposed to estimate the track irregularities from the constructed regression model. The longitudinal-level, alignment, and cross-level irregularities were estimated from the measured car body vibrations and travelling speeds on a regional railway, and the results were compared with the actual track irregularity data. The results showed that the proposed method is applicable for track irregularity management in regional railways.
... Over the past few decades, numerous researchers have directed their attention towards condition monitoring and track maintenance [32][33][34][35]. One of the appropriative approaches that can be employed to achieve this aim is data mining, which is used in railway engineering in various subjects, as mentioned by Villarejo et al. [36], Bergquist and Söderholm [37], Famurewa et al. [38], Sauni et al. [39], and Thaduri et al. [40]. ...
One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as well as maintenance work. Hence, this study investigated the relationship between vertical ballast stiffness and the track longitudinal level. Initially, the ballast stiffness and track longitudinal level data were acquired through a series of experimental measurements conducted on a reference test track along the Tehran–Mashhad railway line, utilizing recording cars for geometric track and stiffness recordings. Subsequently, the correlation between the track longitudinal level and ballast stiffness was surveyed using both frequency-based techniques and machine learning (ML) algorithms. The power spectrum density (PSD) as a frequency-based technique was employed, alongside ML algorithms, including linear regression, decision trees, and random forests, for correlation mining analyses. The results showed a robust and statistically significant relationship between the vertical ballast stiffness and longitudinal levels of railway tracks. Specifically, the PSD data exhibited a considerable correlation, especially within the 1–4 rad/m wave number range. Furthermore, the data analyses conducted using ML methods indicated that the values of the root mean square error (RMSE) were about 0.05, 0.07, and 0.06 for the linear regression, decision tree, and random forest algorithms, respectively, demonstrating the adequate accuracy of ML-based approaches.
... In 2023, Tsunashima et al. [69] developed a system for monitoring railway track conditions, which is crucial for ensuring safety. Traditional track maintenance on regional railways faces financial challenges and a lack of manpower. ...
The kinematic posture of the bogie within comprehensive inspection trains exerts a profound influence on the precision of the measurement systems. This study develops an advanced posture calculation algorithm by integrating data from multiple accelerometers. Rigorous analytical evaluations of the bogie’s central motion posture within the comprehensive inspection train delineate a pronounced escalation in the magnitude of this motion posture concomitant with increased velocities, thereby unveiling a robust correlation in posture outcomes at consistent speeds. Confronting the limitations inherent in the multi-accelerometer amalgamation approach, particularly its diminished sensitivity to posture angle variations precipitated by track geometry anomalies, this research introduces an avant-garde posture compensation paradigm. This paradigm has been corroborated through meticulously constructed experimental models. The rectified outcomes for lateral and vertical displacements, and for roll and nodding angles in posture computation manifest peak errors of 0.09, 0.01, 0.02, and 0.03, respectively, alongside absolute errors amounting to 0.17, 0.04, 0.08, and 0.06, respectively. These empirical findings provide robust substantiation for the analytical dissection of measurement systems accuracy aboard comprehensive inspection trains under diverse operational scenarios. Such revelations bear significant connotations for augmenting the precision and reliability of contemporary railway inspection methodologies.
With the rapid development of the Internet of Things, there exists an urgent necessity for high performance piezoelectric energy harvesters to facilitate the construction of more efficient wireless sensor systems....
The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.
In recent years, condition monitoring technologies have been widely developed aiming at safer railway operation and maintenance cost reduction. Condition monitoring systems of railway for both from onboard and on track have been widely studied thanks to the recent development of sensors. Using equipped sensors on track, as an example of the bogie abnormality detection, the diagonal imbalance of wheel-load for four wheels in a bogie is closely related to the running safety on curve. If serious problem happens in the bogie frame, the diagonal imbalance may change, and wheel load reduction occurs. For these reasons, the monitoring of the diagonal imbalance from track has a potential usefulness. However, in our previous research, the measured imbalance can be varied even though the focused bogies run on the track under almost the same condition such as boarding rate, vehicle velocity and so on. If the cause of variation is clarified, the limit of abnormality detection accuracy can be clarified. Therefore, in this study, variation of the measurement of bogie’s diagonal wheel-load imbalance are investigated. Furthermore, factors that affect the variation of the measurement is investigated using MBD simulation.
