Concerning the optimal balance between the limited budget and maintaining the desired performance and safety level in railway infrastructure, it is necessary to prioritize railway track qualities. ...Although different directives recommend different indices, infrastructure managers often face difficulties in selecting the optimal track quality index. Despite the significant influence that assessing the accurate condition of railway track qualities have on planning maintenance actions, no known track quality index that offers easy construction from a large number of sampling points and is appropriate for the majority of situations has yet been published. This article proposes a stochastic track quality index that considers the uncertainty regarding the quality classification that remains even after the data have been observed. To achieve this, the problem is set into a probability context by selecting a Bayesian framework to characterize the unknown parameters of probabilistic models for track geometry parameters. To demonstrate the efficiency of the approach, the proposed quality index is applied to the data recorded from field observation. To verify the validity of the presented approach, the obtained results are compared to those of deterministic results and reasonable accuracy can be reported. Subsequently, this index enables the infrastructure manager to efficiently prioritize maintenance actions.
Track quality indices can be used as an indicator of rail condition concerning the risk of damage or failure. Previous studies have mainly focused on conventional rail track quality indices and light ...rail tracks have not been addressed properly. In order to fill this gap, this research aims to develop a track quality index which is usable for both conventional and tram rail tracks. In this research dataset of the Melbourne tram network is used. In this research, based on the statistical analysis, track geometry parameters which are statistically significant in the development of the proposed index for the Melbourne tram network are determined. For the evaluation, the predictability performance of the index proposed in this paper is compared with the three major indices in the literature. According to the results of the case study evaluation, the current values of the proposed index has reasonable correlations with its previous values.
The aim of this paper is to demonstrate the possibilities of estimating the track condition using axle-boxes and car-bodies motions described by acceleration signals. In the paper, the results ...presented indicate the condition of tracks obtained from the preliminary investigation on the test track. Furthermore, the results from the supervised runs (on Polish Railway Lines) of Electric Multiple Unit (EMU-ED74) with the prototype of track quality monitoring system installed on-board are described. As Track Quality Indicator (TQI) algorithm, used in the mentioned prototype, a modified Karhunen–Loève transformation is used in preliminary preparation of acceleration signals. The transformation is used to extract the principal dynamics from measurement data. Obtained results are compared to other methods of evaluating the geometrical track quality, namely methods, which apply the synthetic coefficient Jsynth and five parameters of defectiveness W5. The results from the investigation showed that track condition estimation is possible with acceptable accuracy for in-service use and for defining cost-effective maintenance strategies.
The geometrical track degradation is characterized by the evolution over time (or tonnage) of several parameters such as the longitudinal level, the alignment, the gauge, the twist and the cross ...level. Dynamic track inspections allow monitoring the track geometrical quality which is essential to ensure track availability and reliability, passenger safety and comfort and also energy efficiency. The track geometrical quality is guaranteed by performing condition-based maintenance and renewal actions during the life of the track and for that it is crucial to understand the geometrical track degradation process.
In this paper, a stochastic model for characterizing the geometrical track degradation process over time is presented. The Portuguese railway Northern Line is adopted as a case-study and a statistic analysis is performed for different vehicle speed groups, in accordance with CEN 1.
The new contribution of this research is that the Dagum distribution, usually adopted for representing the income distribution, may represent the geometrical track degradation process in terms of the longitudinal level.
The provision of safe, efficient, reliable and affordable railway transport requires the railway track infrastructure to be maintained to an appropriate condition. Given the constrained budgets under ...which the infrastructure is managed, maintenance needs to be predicted in advance of track failure, prioritized and identified risks and uncertainties need to be considered within the decision-making process. This paper describes a risk-informed approach that can be used to economically justify railway track infrastructure conditions by comparing on a life-cycle basis infrastructure maintenance costs, train operating costs, travel time costs, safety, social and environmental impacts. The approach represents a step-change for the railway industry as it will enable economic maintenance standards to be derived which considers the needs of the infrastructure operator, but also those of users, train operating companies and the environment. Further, the risk-informed capability of the tool enables asset managers to deal with uncertainties associated with forecasting costs and the effects of track maintenance, and unavailability of data. The Monte Carlo simulation technique and a Fuzzy reasoning approach are used to address safety data uncertainties through probabilistic risk assessment allied to expert opinion. The approach is illustrated using data from three routes on the UK mainline railway network. The results demonstrate that the approach can be used to support strategic and tactical levels of railway asset management to inform plausible design and maintenance strategies that realise the maximum benefit for the available budget.
Track quality evaluation is fundamental for track maintenance. Around the world, track geometry standards are established to evaluate track quality. However, these standards may not be capable of ...detecting some abnormal track geometry conditions that can cause considerable vehicle-body vibration. And people gradually realized that track quality evaluation should be based not only on track geometry but also on vehicle performance. Vehicle-body vibration prediction is beneficial for locating potential track geometry defects, and the predicted accelerations can be used as an auxiliary index for assessing track quality. For this purpose, this paper gives a method to predict vehicle-body vibration based on deep learning, which represents one of the newest areas in artificial intelligence. By integrating convolutional neural network (CNN) and long short-term memory (LSTM), a CNN-LSTM model is proposed to make accurate and point-wise prediction. To achieve the optimal performance and explore the internal mechanism of the model, structural configurations and inner states are extensively studied. CNN-LSTM can take advantage of the powerful feature extraction capacity of CNN and LSTM, and outperforms the fully-connected neural network and the plain LSTM on the experimental data of a high-speed railway. In detail, CNN-LSTM has superior performance in predicting vertical vehicle-body vibration below 10 Hz and lateral vehicle-body vibration below 1 Hz. Moreover, analysis shows that the predicted vehicle-body acceleration can act as a performance-based evaluation index of track quality.
As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have ...become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.
In the track quality analysis, numerical values representing the relative condition of track geometry called track quality indices (TQIs) are calculated along a specific track segment. Segments are ...defined as linear track geometry datasets with the homogeneous characteristics of factors affecting geometry degradation. The 200m-long analytical segment is used most often on inter-city conventional and high-speed rail networks. However, in the case of the small urban rail networks, the homogeneity of track-geometry degradation influential factors is very low. This segment length is usually too long for efficient track maintenance or reconstruction with minimal disruption of the urban traffic. This paper explores the effect of reducing the analytical segment length in the condition assessment of the tram network in the City of Osijek, Croatia. The research had two main objectives: (1) to assess the narrow-gauge tram-track geometry quality through the application of the established synthesized TQIs, and (2) to analyze how a change in the analytical segment length affects this assessment. Two synthesized track quality indices—one based on a weighted value and the other on a standard deviation of measured track geometry parameters—were calculated for the 27.5 km of tracks on consecutive 200-, 100-, 50-, and 25 m long analytical segments. The comparative analysis of the TQIs’ calculation results showed that the reduction in the segment length increased the resolution of the track quality analysis in both cases, while the index based on a weighted value of geometry deviations proved less sensitive to this reduction. These results contribute to further segmentation process establishment and TQIs implementation on tram infrastructure.
AbstractAs railway maintenance increasingly requires strict inspection and accurate repair, it is urgent to establish a reasonable assessment method for identifying the risks in high-speed railways. ...Severe track irregularities can intensify the vibration of vehicles and infrastructure and reduce vehicle stability and ride comfort. To more accurately identify the risks in railway tracks, this paper presents the acceleration indexes associated with ride comfort and an acceleration-based track quality index (ATQI) method. The proposed method involves a dynamic weighting factor that can extract the most sensitive information from acceleration and geometrical parameters. Results show that the weight change reflected the cumulative effect of fluctuation in each parameter in the past periods. In a case study, the ATQI method was able to identify seven risky sections that exceed the limit value, while only four risky sections were identified by the conventional track quality index. This demonstrates that the ATQI is more effective and efficient in predicting risks than the track quality index.
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