Track Condition Monitoring Research Articles

Railroad Track Condition Monitoring Using Inertial Sensors and Digital Signal Processing: A Review

Inertial sensors such as accelerometers and gyroscopes have been widely used since the early 1990s to monitor the condition of transportation assets. Recent improvements in their performance, a reduction in cost, and sensor miniaturization has resulted in a growing interest in expanding their use. This research is an extensive and systematic review of their application considerations, challenges, and opportunities for improvements in railroad track condition monitoring. Research questions were developed to guide the selection of relevant articles from databases. The authors report key findings in the areas of sensor specification, sensor location, and sensor signal processing.

A Track Condition Monitoring System using In-service Vehicle for Regional Railways

A Track Condition Monitoring System using In-service Vehicle for Regional Railways

A practical approach to tramway track condition monitoring: vertical track defects detection and identification using time-frequency processing technique

Abstract This paper presents an automatic method for detecting vertical track irregularities on tramway operation using acceleration measurements on trams. For monitoring of tramway tracks, an unconventional measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs. Accelerations are processed to obtain the vertical track irregularities to determine whether the track needs to be repaired. The automatic detection algorithm is based on time–frequency distribution analysis and determines the defect locations. Admissible limits (thresholds) are given for detecting moderate and severe defects using statistical analysis. The method was validated on frequented tram lines in Budapest and accurately detected severe defects with a hit rate of 100%, with no false alarms. The methodology is also sensitive to moderate and small rail surface defects at the low operational speed.

Track Condition Monitoring using Machine Learning Technique for Regional Railways

Track Condition Monitoring using Machine Learning Technique for Regional Railways

Determination of railway track longitudinal profile using measured inertial response of an in-service railway vehicle

The use of sensors fixed to in-service trains has the potential to provide real-time track condition monitoring to inform maintenance planning. An Irish Rail intercity train was instrumented for a period of 1 month so that a numerical method developed to find track longitudinal profile from measured vehicle inertial responses could be experimentally tested. A bogie-mounted accelerometer and gyrometer measured vertical acceleration and angular velocity as the train made regular service operations between Dublin and Belfast on the island of Ireland. Cross entropy optimisation is used to find a track longitudinal profile that generates a numerical inertial response that best fits the measured response. Tolerance limits are used to inject variance where required to ensure a good match between measured and modelled signals. A section of track with known track settlement history is selected as a case study. A level survey was undertaken during the measurement campaign to characterise the longitudinal profile through the test section. Bandpass filters are used to compare inferred profiles and the surveyed profile. Good agreement is found between the two profiles although improvements in accuracy and reproducibility are required before conformance with current standards is achieved.

Fatigue Life Assessment Method for Prestressed Concrete Sleepers

Concrete sleepers are one of the most important applications of a railway track system. Researchers have previously studied the impact load characteristics and ultimate load carrying capacity of a prestressed sleeper but research on the fatigue life of prestressed concrete sleepers is limited. Prestressed concrete sleeper fatigue damage is mainly due to the accumulation of defects, caused by the repeated load of wheel-rail interaction. Fatigue load, fatigue characteristics and the existing design methods of prestressed concrete sleeper are summarized in this paper. The commonly used fatigue assessment methods of concrete structures are also evaluated. Based on the results of former research result, this article presents a convenient fatigue life assessment method for a prestressed concrete sleeper, and contrasts with the test results. The insight information gained can be used to evaluate the service performance and predict the fatigue life of the concrete sleeper, as well providing design flexibility and broadening the design principle. The outcome of this study may also improve the rail track maintenance and inspection criteria, in order to establish an appropriate track condition monitoring network in practice.

Impact Capacity Reduction in Railway Prestressed Concrete Sleepers with Surface Abrasions

Railway sleepers (also called ‘railroad tie’ in North America) embedded in ballasted railway tracks are a main part of railway track structures. Its important role is to transfer the loads evenly from the rails to a wider area of ballast bed and to secure rail gauge and enable safe passages of rolling stocks. By nature, railway infrastructure is nonlinear, evidenced by its behaviours, geometry and alignment, wheel-rail contact and operational parameters such as tractive efforts. Based on our critical review, the dynamic behaviour of railway sleepers has not been fully investigated, especially when the sleepers are deteriorated by excessive wears. In fact, the ballast angularity causes differential abrasions on the soffit or bottom surface of sleepers (especially at railseat zone). Furthermore, in sharp curves and rapid gradient change, longitudinal and lateral dynamics of rails increase the likelihood of railseat abrasions in concrete sleepers due to the unbalanced loading conditions. This paper presents a structural capacity of concrete sleepers under dynamic transient loading. The modified compression field theory for ultimate strength design of concrete sleepers under impact loading will be highlighted in this study. The influences of surface abrasions, including surface abrasion and soffit abrasion, on the dynamic behaviour of prestressed concrete sleepers, are firstly highlighted. The outcome of this study will improve the rail maintenance and inspection criteria in order to establish appropriate and sensible remote track condition monitoring network in practice. Moreover, this study will also improve the understanding of the fundamental dynamic behaviour of prestressed concrete sleepers with surface abrasions. The insight into these behaviours will not only improve safety and reliability of railway infrastructure but will enhance the structural safety of other concrete structures.

Track geometry estimation of a conventional railway from car-body acceleration measurement

This study proposes a track condition monitoring technique using car-body acceleration that can be easily measured by an in-service vehicle for the sake of an increase in safety of railway transportation. This paper demonstrates the possibility of estimating track irregularities of conventional railway tracks using car-body acceleration only. The methodology proposed uses inverse dynamics to estimate track irregularity from car-body acceleration, applying a Kalman filter to solve this problem. This technique estimates the track irregularity in the longitudinal plane (track geometry and 10m-chord versine). The Kalman filter is able to apply to inverse analysis by expressing track geometry as a random walk model, and incorporating the model in an equation of state. The estimation technique can support a change of the vehicle velocity by selecting an appropriate impulse response in the measurement equation for the vehicle velocity. Estimation results in simulation and full scale tests revealed that the proposed estimation technique is effective for track condition monitoring with acceptable accuracy for conventional railways.

Defining Rail Track Input Conditions Using an Instrumented Revenue Vehicle

To model the dynamic response of a train running over a given length of rail, the input conditions must be adequately defined. This paper explores the use of an instrumented rail vehicle to assist in the definition of this input condition. The benefits of using instrumented rail vehicle for condition assessment of track and rolling stock are gaining popularity. Railway track and rolling stock condition monitoring is essential in ensuring the safe and efficient function of railway systems. The ability to use an instrumented revenue vehicle for the condition assessment of rail tracks is particularly significant because this capability will not require track access during inspection. This instrumented vehicle can also provide useful data for the definition of the rail track input conditions for the assessment of the stability of a rail vehicle when traversing along the track. This information can be used to establish the wagon speed limits to ensure safe operation of the asset. To be able to predict the dynamic response of a rail vehicle, the dynamic input conditions imposed by the track will need to be identified. This paper will discuss some initial studies on the use of an instrumented rail vehicle to define the input conditions imposed by the rail track. The ability to define the rail input conditions is demonstrated by associating the results with features on the track that are identifiable.

Improved Railway Track Irregularities Classification by aModel Inversion Approach

Over time railway networks have become complex systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures aremade up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual aswell as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of kilometers of railway tracks are almost continuously monitored. Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a novel hybrid approach is presented. To this end, a simplified vehicle suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded system responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound basis of subsequent data-driven condition monitoring analyses.In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically.

Urban rail track condition monitoring based on in-service vehicle acceleration measurements

Condition monitoring of railway tracks is essential in ensuring the safety of railway systems. Traditional track recording vehicles (TRV) can only carry out the track inspection in the midnight after all the service vehicles are off duty. One track line can be inspected only once or less in a month. The severe track condition degradation cannot be detected timely and there is potential safety hazard for the railway system. Therefore, the track condition monitoring based on in-service vehicle has been paid more attention in the recent years. In this paper, the track condition monitoring based on the bogie and car body acceleration measurements is presented. The track alignment information which can be derived from the bogie and car body sensor is investigated by using a mathematical model and the frequency response analysis. The track irregularities can be estimated by using the bogie and the car body acceleration sensors. The acceleration signals are preprocessing with a DC filter and a low pass filter. After that, the track alignments are obtained by double integrating the signals. Field tests are carried out in Shang Hai metro Line 1 for demonstrating the effectiveness of the proposed track inspection system.

G1800104 在来線における車体振動加速度を用いた軌道狂い推定に関する研究

This study proposes the track condition monitoring technique using car-body acceleration easily measured by an in-service vehicle for the sake of increase in safety of railway transportation. This paper demonstrates the possibility to estimate the track irregularities of conventional railway tracks using car-body acceleration only. In an inverse problem to estimate track irregularity from car-body acceleration, a Kalman filter was applied to solve the inverse problem. This estimation technique is utilized to estimate the track irregularity in longitudinal level (track geometry and 10m-chord versine). The Kalman filter became able to apply to inverse analysis by expressing track geometry in a random walk model, and having incorporated the model in an equation of state. The estimation technique can support a change of the vehicle velocity by selecting impulse response in the measurement equation for the vehicle velocity. Estimation results revealed that the proposed estimation technique is effective for track condition monitoring with acceptable accuracy for conventional railways.

2B21 Track geometry estimation of railway from car-body acceleration(Condition Monitoring-Infrastructure)

This study proposes the track condition monitoring technique using car-body acceleration easily measured by an in-service vehicle for the sake of increase in safety of railway transportation. This paper demonstrates the possibility to estimate the track irregularities of conventional railway tracks using car-body acceleration only. In an inverse problem to estimate track irregularity from car-body acceleration, a Kalman filter was applied to solve the inverse problem. This estimation technique is utilized to estimate the track irregularity in longitudinal level (track geometry and 10m-chord versine). The Kalman filter became able to apply to inverse analysis by expressing track geometry in a random walk model, and having incorporated the model in an equation of state. The estimation technique can support a change of the vehicle velocity by selecting impulse response in the measurement equation for the vehicle velocity. Estimation results revealed that the proposed estimation technique is effective for track condition monitoring with acceptable accuracy for conventional railways.

2P25 Development of track condition monitoring system using in-service vehicle and monitoring examples(Shotgun Session)

Condition monitoring of railway tracks are essential in ensuring the safety of railways. Preventive maintenance repairing before an accident occurs is an important challenge. Current inspection method of tracks is by track maintenance staff and by track inspection car. These method can be highly accurate inspection, but it is limited to use in local lines. Therefore the condition monitoring techniques that can be adopted in the local railway is required. In the context, a condition monitoring system of railway track using in-service vehicle has been developed. In-service vehicles equipped with sensors and GPS systems can act as probes to detect and analyze real-time vehicle vibration. Such trains are known as "probe vehicles". Recently, a compact size on-board sensing device has been developed. This paper describes the track condition monitoring system using the compact size on-board sensing device and the diagnosis software. It is known that a car-body acceleration is effective for making rough estimate of track condition. This diagnosis software uses RMS of car-body acceleration as evaluation index for basic analysis. However, the RMS values can't isolate the track faults due to the lack of frequency information. Thus, continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are employed to extract different type of the track faults. The validity of this software was evaluated by simulation studies. Simulation results showed that the diagnosis software is useful for track condition monitoring. Monitoring experience in a local railway line showed that the system is effective for practical use.

小型レール診断装置を用いた軌道状態診断システムの開発

小型レール診断装置を用いた軌道状態診断システムの開発

G1800103 在来線用レール状態診断システムの開発

Condition monitoring of railway tracks is essential in ensuring the safety of railways. In-service vehicles equipped with sensors and GPS systems can act as probes to detect and analyze real-time vehicle vibration. Such trains are known as "probe vehicles". Recently, a compact size on-board sensing device has been developed. This paper describes the track condition monitoring system using the compact size on-board sensing device and the diagnosis software. The diagnosis software provides a function to detect track irregularities using RMS of the car-body acceleration. It also provides a function to analyze in time-frequency domain using wavelet transform. Monitoring experience in a local railway line showed that the system is effective for practical use.

Track geometry estimation from car-body vibration

Track maintenance works based on track geometry recordings are essential to enhance the safety and comfort of railway transportation. The track condition monitoring system is mainly used for the choice of area needing track tamping works for the purpose of the good riding comfort. An advantage of car-body acceleration measurement devices is their simple structures, which make it easier to carry out maintenance. However, the car-body acceleration waveform is considerably different from track geometry. This paper demonstrates the possibility to estimate the track geometry of Shinkansen tracks using car-body motions only. In an inverse problem to estimate track irregularity from car-body motions, a Kalman Filter (KF) was applied to solve the problem. Estimation results showed that track irregularity estimation in vertical direction is possible with acceptable accuracy for real use.

Track Geometry Estimation from Car-Body Motions of Railway Vehicle

To enhance the safety and reliability of railway transportation, it is one of the most important tasks to check the track condition frequently and accurately. This paper describes a track condition monitoring technique using car-body motions. In an inverse problem to estimate track geometry in longitudinal level from vertical car-body acceleration and pitching rate measured by in-service vehicle, dynamic programming filter and Kalman filter were applied. Study results confirmed that proposed solutions can be used to estimate track geometry in level with desired precision.

J102026 車両モデルを用いた新幹線軌道の状態監視に関する研究

Railway tracks receive axle load by train runs and the track irregularities grow. As the track irregularity deteriorates ride comfort and running safety, track condition monitoring for maintenance is one of the most important tasks for railway companies. The Shinkansen track is measured by track geometry cars. A running frequency of the track geometry car is every 10 days. But it is difficult to check the Shinkansen track at designated time. This study proposes track irregularity estimation techniques from a car-body acceleration of vehicle body. As the acceleration measurement of a car body is much easier than that of an axle-box, a high frequency measurement is possible and it enables preventive track maintenance planning. An inverse analysis technique is applied to estimate the track irregularity. This technique is frequently utilized to determine unknown input signal (track irregularity) from a known output signal (car-body acceleration). A Kalman filter is used to estimate the track irregularity from observed car-body acceleration. It can be concluded that the track irregularity estimation in vertical direction is possible with acceptable accuracy for practical use with the proposed method.

1021 状態空間モデルを用いた軌道の状態監視(OS17-2 交通システムのダイナミクスと制御II,OS17 交通システムのダイナミクスと制御)

To enhance the safety and reliability of railway transportation, it is one of the most important tasks to check the track condition frequently and accurately. This paper describes the track condition monitoring technique using the car-body acceleration. In an inverse problem to estimate track irregularity from car-body acceleration measured by in-service vehicle, state space model and Karman filter is applied. As a result, it can be concluded that track irregularity in longitudinal level is able to estimate with desired precision.