Track Recording Research Articles

Evaluation of onboard sensors for track geometry monitoring against conventional track recording measurements

The main objective of this paper is to assess the estimation of track condition parameters using onboard micro-electro-mechanical-system (MEMS) accelerometers. A prototype of an onboard data acquisition system was designed and installed on a track recording car (TRC) and a measurement campaign was conducted on an extensive portion of the Brisbane Suburban railway network. Comparison of the accelerometer-based results vs TRC recordings have shown that accelerometers installed on the bogie are the best compromise between proximity to the source and insensitivity to impulsive noise. It was found that two vertical bogie accelerometers (left and right) provide a good quantitative estimate of vertical alignment and that strong correlations with TRC measurements exist for lateral MEMS accelerometer measurements (horizontal alignment). These findings suggest that two bogie MEMS accelerometers with vertical and lateral measurement axes are effective in estimating geographical distributions of vertical/horizontal alignment.

Innovative Trends in Railway Condition Monitoring

Provision of functional safety of a railway track requires complementing the existing system of assessment of the railway track condition with the stress-strain behaviour assessment of the rail substructure, i.e. the assessment of the effect of track structure elasticity on the change of its geometric parameters during rolling stock passage over time. There is a lack of available theoretical and practical information about any pivotal changes in this field of science. The subject of the study is the in-situ elasticity indicators of a railway track and geometric monitoring indicators of railway structure condition or similar indicators obtained by numerical processing of data provided by the measuring systems of a track recording car and enabling the determination of change of irregularity parameters over time taking into account of the tonnage passed. The outcome of the investigation is the proposed approach to the development of an unparalleled diagnostic loading system which has been the missing part of the “puzzle” in the existing system of integrated assessment of the technical condition of a railway track.

An unsupervised real-time spike sorting system based on optimized OSort

Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems. Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. The CC method not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time. Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5–80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68 µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort’s ability to handle variations in neural activity caused by electrode drifting is also demonstrated. Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain–computer interfaces.

Simulation of transient dynamic response of a locomotive considering its flexibility and rigidity

Time-domain or transient dynamic response method is the most traditional method used to evaluate the dynamic response of a vibratory system subjected to deterministic inputs. The inertia or damping effects are very crucial in the time scale of loading. The present work determines the time domain analysis of railway locomotive using finite element analysis for Indian tracks. The track inputs are modelled as an ellipsoidal bump, and variations in displacement at different key locations of the truck and carbody models are plotted subjected to standard loading conditions. The response plot of the front and rear locations of the locomotive truck and carbody indicate that these regions are more responsive to wheel inputs in comparison to the mid portion due to unbalanced mass distribution. In the further study vertical-lateral random inputs are measured through track recording car and modelled to express in the form of PSD. These random inputs are provided to finite element modelled flexible bogie frame, and acceleration response is determined. The results obtained are compared with the same obtained from rigid bogie frame formulated using an analytical model. The results obtained from the flexible and rigid body models are found to be in good agreement.

Connection Between Track Geometry Quality and Dynamic Vehicle Response at Various Speeds

Track geometry measurements are widely used for describing track quality. However, derailments and track deterioration are caused by forces arising in vehicle-track system. This research focuses on two types of vehicle response. Firstly, the influence of the longitudinal level irregularities on the vertical wheel-rail forces was examined. Secondly, the correlation between the lateral axle box acceleration and the cross level irregularities was investigated. Track geometry and vehicle response data were acquired simultaneously by a track recording car, formed from a passenger car, at various speeds up to 130 km/h. Vehicle-track forces were calculated based on accelerometers mounted on the car body, bogies and axle boxes, considering mass and moment of inertia. Non-linear regressions resulted in vertical vehicle-track force estimation functions. It was proven that the use of second spatial derivatives of the longitudinal level gave a better estimation than the use of reference TQIs according to European Standard EN 13848-6. A linear relationship was found between the speed and standard deviation of vertical vehicle-track forces. On straight sections with constant speed, correlation coefficients of around 0.8 were found between second spatial derivatives of cross level and lateral axle box acceleration.

Analysis of the Relationship between Rail Cant and Track Gauge Based on the Data Obtained by Track Recording Cars

The increase in the service life of track superstructure has always been an important task since this allows reduction in the cost of repairs and current maintenance of the track.The objective of the work is to determine the mutual influence of the rail cant and the track gauge based on the results of measurements carried out by track recording cars, as well as their impact on the stress-strain state of the rail. The analysis was based on statistical and the finite element methods. Using statistical methods, it was determined that the rail cant and track gauge values do not have a normal distribution and have a weak feedback. The reasons were explained in the conclusions.To determine with the finite element method contact stresses in the rail head on the track sections available in the sample, with different combinations of rail cant and track gauge, a model with full geometric similarity was developed.The results obtained from the calculations demonstrate that the deviation of the cant within the range from 1/15 to 1/30 leads to an increase in stresses by more than 20 %, while changing the track gauge has a weak effect on the stress-strain state of the rail head. The maximum increase in contact stresses, at the analysed section, was 97 %, with a cant of 1/990 and a track gauge of 1526 mm.The growth of contact stresses leads to the formation of fatigue cracks and, as a consequence, to the defects and then to replacement of the rail. To increase the service life of the rails, it is recommended to monitor the condition of fasteners and compliance with the technology of current maintenance works, as well as to reconsider tolerance of the deviation of the cant both upward and downward.

Influence of Track Defects on the Track-Loads and Confidence Interval of a Track Recording Car

The Railway track, is simulated as a beam on elastic foundation with damping. The motion of a vehicle is simulated by the 2nd order differential equation of motion; the input to the system “vehicle-rail” is the form of the Track Geometry which “acts” as a “signal”. The defects with short and long wavelength influence the value of the dynamic component of the acting loads on the railway track and they are recorded in the frame of the Quality Control of the Railway Track, which presents a determined Confidence Interval depending on its “measurement-base”.

Track geometry quality assessments for turnouts

Turnouts are necessary, frequent and cost intensive. Despite the railway sector's focus on automation, the condition assessment of turnouts is still mostly done manually by responsible staff inspecting the turnout. While system's safety is guaranteed by this approach, drawbacks are high costs, subjectivity of assessments and a missing opportunity of prognoses. Most importantly, we will not expect to have the workforce for this regime in future years. By assisting responsible staff with data-based quality assessments, save and time optimised inspection can also be guaranteed with labour force reductions. For providing this, we use data from the track recording car, apply a post positioning procedure and calculate quality indices describing track geometry quality of turnouts in an objective way. The visualisation of these indices over time lead to data sheets which can be used by the responsible staff for maintenance planning. Additionally, the indices build the basis for predictive maintenance regimes for turnouts and open several possibilities for practical and academic enhancements regarding asset management of turnouts.

Deriving parameters for characterisation of the track quality in relation to environmental vibration

Irregularities in track geometry; like insulated joints, switches and crossings, etc. and track stiffness variations in transitions zones near bridges or culverts play a role in the dynamic forces in the wheel-rail interface of train and track. These are important sources for ground-borne vibrations. In the Netherlands the track is monitored to facilitate maintenance management using a track recording coach (TRC). Data of these TRC measuring campaigns are stored and available for stakeholders working in field of rail maintenance or railway engineering. A database was build using historical data of the TRC measurements back to 2013 together with meta data of track components such as insulated joints, switches, crossings, culverts, etc. The track geometry data (height and shift) have been post processed towards two newly designed spectral parameters, both of them were expected to correlate with ground borne vibration at a distance from the track. One characterises discontinuities, the other continuous track. The paper will show how these parameters can be used to compare track quality and quality of track components with regard to vibrations. The study analyses differences in track quality near transition zones in relation to vibration levels.

Track geometry monitoring by an on-board computer-vision-based sensor system

This paper illustrates some outcomes of the EU project Assets4Rail, funded by the Shift2Rail Joint Undertaking, as well as on-going research developments.Nowadays, many Track Recording Vehicles (TRV) are equipped with contactless optical/inertial systems to monitor Track Geometry (TG). The dedicated trains and sophisticated measurement equipment are costly to acquire and maintain. Therefore, the interval between two TRV recordings of the TG on the same line section cannot be too short (twice per month to twice per year).Recently, railway operators are showing increasing interest in the use of commercial trains to monitor track condition in a cost-effective manner.TRVs' expensive and constantly maintained optical systems make them unsuitable for commercial fleets. On-board sensor systems based on indirect measurements such as accelerations have been developed in various studies. While detection of the vertical irregularity is relatively straightforward through double-integration of the recorded acceleration, it is yet an unsolved issue for lateral irregularities due to the complicated relative wheel-rail motion.The investigated system conceptually combines on-board measurements of wheel-rail lateral relative position and lateral axlebox acceleration to detect rail alignment variations. It includes a functional prototype of an on-board Computer Vision (CV) sensor capable of monitoring the Lateral Displacement of the Wheel with respect to the Rail (LDWR). Further progress is aiming at the reduction of the measurement errors due to wheelset lateral displacements relative to the track, which is essential for calculating rail alignment.The sensor system prototype was tested in Italy at 100 km/h on the Aldebaran 2.0 TRV of RFI (Rete Ferroviaria Italiana), the main Italian Infrastructure Manager. It was found that the estimated lateral displacement correlates to the lateral alignment acquired by the Aldebaran 2.0 commercial TG inspection equipment.Moreover, a Simpack® simulation of a multi-body model of Aldebaran 2.0 provides the axlebox acceleration values not measurable during the test runs, to evaluate the relationship between them, LDWR and track alignment.

Feasibility of Onboard Smartphones for Railway Track Geometry Estimation: Sensing Capabilities and Characterization

The performance and sensitivity of smartphone sensors developed rapidly in recent years. Due to their accessibility and low costs compared to other industrial solutions, the use of smartphone sensors has become more and more common. In this article, the validity and reliability of a smartphone application in railway track geometry estimation are tested on a conventional rail line. This work focused on Galaxy S-series smartphones of Samsung and proposes an evaluation of the onboard sensing capabilities of their inertial sensors with the comparison of synchronous measurements by a multi-functional Track Recording Vehicle equipped with a contactless Track Geometry- (TGMS), and a Vehicle Dynamic Measuring System (VDMS). The raw accelerometer recordings showed a high-degree correlation with VDMS in both signal magnitude and waveform. The accuracy of gyroscope angular tracking in heading and pitching angle calculation was in the range of 0.2°–0.6°, which allowed the acceptable estimation of the central angle and the radius of horizontal curves. Based on the kinematic analysis techniques, the roll flexibility coefficient of the vehicle was determined, which allowed calculating the cross-level and the twist of the track. Furthermore, the local extreme values of the roll-rate gyro correlate with the isolated track geometry defects of the track twist gathered by TRV’s TGMS. Despite its limitations, the application of smartphones represents a prospective technological opportunity to explore new approaches and support rail asset management.

Application of Spatial-Temporal Behavioral Trajectory Analysis in the Space Design of Digital Villages

From the perspective of the environment-behavior science, this paper takes the space design of a digital village in Youzhaqiao Village on the outskirts of Guangshui City, for example, to record and track the behavior of residents and tourists in the area via mobile phone motion track recording software. Based on the collection, selection, and collation of the trajectory data, the valid data has been counted and analyzed; finally, the field investigation and questionnaires have been integrated to propose optimization strategy for the use and design of the space of Youzhaqiao Village from aspects such as human perception, cognition, and behavior.

Deep neural network for diagnostics of railway track components

The problem of increasing the speed of railway transportation and ensuring reliability is associated with constant monitoring of the condition of the railway tracks. The modern track measuring cars are equipped with video cameras and computer equipment for processing the received information. However, manual processing of data by operators in real-time is not possible. The article proposes a deep convolutional neural network for automatically recognizing and classifying defects in rail joints on rail track images. The rail video observation forms the image array during the passage of the track recording car. The formation of classes of rail joints is described. Regular rail joints with connectors, insulating joints, and welded joints are considered. Additional classes are identified, corresponding to various anomalous configurations of rail joints in the images. A modified structure of a pre-trained deep convolutional network is constructed. When preparing training samples, the actual images of rail joints were supplemented with artificial images obtained by affine transformations. The process of training and testing the classifier based on the developed convolutional network is described. The Transfer Learning is used to train the neural network. As a result of the experiments, the accuracy of classifying rail joints and detecting defects was at least 96%.

Simulation of the inherent structural irregularities of high-speed railway turnouts based on a virtual track inspection method

Track geometry parameters measured by a track recording car at a turnout are often hard to interpret due to the concomitant geometric complexity, especially for a high-speed railway turnout whose running edge is not interrupted. The virtual track inspection (VTI) method integrated with a multibody system model is proposed to reveal the longitudinal level and alignment of high-speed railway turnouts when the train passes in the main line and in the branch line. Firstly, the principle of inertial measurement and its implementation in MBS are introduced. Then, the VTI method is applied to the vehicle-turnout dynamics using a single-layered co-running track model and validated on tangent and curved tracks in the spatial and wavelength domain. Zero-phase Filters for wavelength ranges 3–25 m and 1–5 m are necessary to eliminate interference from long waves, and to better identify the switch and crossing, respectively. Finally, the effect of different track foundations is studied, including the two-layered co-running track model and the finite element model on the VTI method.

Analytical forecasting of long-term railway track settlement

Railway tracks undergo plastic settlement when subject to repeated train loading. This occurs differentially along the track rather than in a uniform manner, and the profile is a key parameter when scheduling track maintenance operations. Therefore this paper presents a novel numerical approach to predict track irregularity evolution. The model combines empirical settlement laws with finite element theory, where the track-ground structure is modelled explicitly, and multi-body train-track interaction is considered. The stresses induced by rolling stock are solved using a hybrid frequency-wavenumber and time–space approach, considering non-linear track-soil material behaviour. It has several novelties: 1) after every load passage, the track profile is updated before applying the next load, meaning the train-track interaction is constantly evolving; 2) new empirical settlement laws are derived that account for evolving train-track forces and track profiles; 3) fully 3D stress fields in the track and ground are considered. First the model is described, before validating its prediction of track geometry evolution, captured from track recording vehicles. Next, it’s used to show that modelling error is introduced if the geometry isn’t updated frequently (e.g. after every load passage). Finally, a parametric study shows track subgrade material properties have a marked effect on track settlement.

Experimental measurement of track irregularities using a scaled track recording vehicle and Kalman filtering techniques

The actual trend of railway industry on track surveying is heading up the development of fast automated methods for continuous monitoring of track quality. This paper presents a model-based methodology for the estimation of lateral and vertical track irregularities from different sensors mounted on a Track Recording Vehicle (TRV): an Inertial Measurement Unit (IMU), a gauge sensor and a position encoder. The proposed methodology, based on the Kalman filtering technique, allows a fast and accurate measurement of track irregularities, without the use of a total station (which usually makes the measurement process very slow). The proposed method has been validated through an experimental campaign carried out on a 90 m long 1:10, scaled track facility and a scaled TRV at the University of Seville. The use of the scaled TRV allows the measurement of the 90 metres long scaled track at an operation velocity of V = 0.7 m/s in only two minutes. The results of the estimation of track irregularities have been compared with a previous measurement of the mentioned scaled track using manual means, showing a good agreement: with errors lower than 0.7 mm in the short wavelength range D1, which is the most influential in the dynamic behaviour of the vehicle. Additionally, the obtained results have been analysed and compared in the different wavelength ranges, according to standards.

Pengujian Perangkat Lunak Aplikasi Studio Musik Nedd Audio Production Menggunakan Teknik Boundary Value Analysis

NEDD Audio Production is a recording studio that provides on-site music or track recording services and track sales. The owner still uses the manual system for scheduling records and still uses social media for track sales. This causes inefficient owner performance. To create a website that provides booking services for recording schedules and purchasing tracks that can help the owner in placing the recording schedule and purchasing tracks efficiently. The design used to create a website using UML (Unified Modeling Language), which consists of Use Case Diagrams.. Meanwhile, the programming language uses PHP and MySQL as a database. The implementation of this website uses a third-party service, namely Midtrans, which is a payment gateway service provider website from Indonesia

MUHAFIZ: IoT-Based Track Recording Vehicle for the Damage Analysis of the Railway Track

Fault diagnosis plays a major role in railway condition monitoring, as early diagnosis of the emerging faults can save valuable time, reduce maintenance costs and, most significantly, help save people's lives. However, the conventional data-driven methods used to diagnose track faults, especially in underdeveloped countries, use push trolley/train-based track recording vehicles (TRV) that rely heavily on manual extraction of track data. It is a very demanding process and significantly affects the final results due to its reliance on human judgment in assessing track conditions and its suboptimal performance. In contrast, with the advent of IoT-based smart inertial measurement units, the data-driven fault diagnosis became a core component in the smart industrial automation safety system. We proposed, Muhafiz, a prototype that is an automated and portable TRV with a novel design based on axle-based acceleration methodology for rail track fault diagnosis. Our contribution concluded, based on site-specific experimentation, that Muhafiz is 87% more efficient than the traditional push trolley-based TRV mechanism.

Response based track profile estimation using observable train models with numerical and experimental validations

Condition monitoring of railway tracks is essential in guaranteeing the running safety of railways. Track profiles are the primary source of external excitation for a train system. While Track Recording Vehicle is often utilized for maintenance purposes, this particular vehicle is expensive and difficult to use for small railway operators. Therefore, track profile estimation through in-service vehicle response measurements, which potentially provides efficient and frequent measurement, has been studied. However, the quantitative evaluation of the vertical and lateral track profile irregularities is still challenging as the inverse analysis solutions are sometimes inaccurate and even unstable. In this paper, numerical analyses are first carried out to evaluate track profiles from acceleration and angular velocity responses measured on a train car body. For the inverse analysis, an Augmented State Kalman Filter is utilized to solve the problem using 4 degrees of freedom observable train models. The sensor installation locations are investigated through observability rank condition analysis with different measurement layout. Secondly, a field experiment is carried out in a local Japanese in-service railway network to estimate track profile from car body motions. Smartphones are utilized for the field test measurements as prevalent sensing devices. The effectiveness of the proposed approach is demonstrated with the observable train model. Numerical analyses and field experiments clarify the proposed track profile estimation

Identify severe track geometry defect combinations for maintenance planning

ABSTRACT Combined track defects (multiple types of track defects occurring in the same vicinity of a track section) pose greater challenges and risks for train operational safety. This study proposed a method to identify severe track defect combinations that pose greater risks for vehicle derailments. The method includes three main stages. The first stage uses literature review, industry survey, and Track Recording Car data analysis to determine track defect combinations that exist in reality and are of interest to the infrastructure managers. The second stage uses single vehicle simulations to further fine-tune the focus and select the more severe combinations. The third stage uses comprehensive simulations to determine the increases of derailment risks due to combined track defects. More than 5,000 simulations were conducted in this study using supercomputers. A tabular tool was developed to help infrastructure managers to adjust maintenance reactions when track defects are found in combinations.