Measurement of Railway Track Geometry: A State-of-the-Art Review

The worldwide increase in frequency of traffic for passenger trains and the rise of freight trains over the recent years necessitate the more intense deployment of track monitoring and rail inspection procedures. The wheel-rail contact forces, induced by the static axle loads of the vehicle and the dynamic effects of ground-borne vibration coming from the track superstructure, have been a significant factor contributing to the degradation of the railway track system. Measurements of track irregularities have been applied since the early days of railway engineering to reveal the current condition and quality of railway lines. Track geometry is a term used to collectively refer to the measurable parameters including the faults of railway tracks and rails. This paper is aiming to review the characteristics of compact inertial measurement systems (IMUs), their components, installation, the basic measures of the quality of the track using motion sensors, like accelerometers, gyroscopes and other sensing devices mounted on different places of the vehicle. Additionally, the paper briefly discusses the fundamentals of inertial navigation, the kinematics of the translational and rotational train motions to obtain orientation, velocity and position information

Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map

Train localization is safety-critical and therefore the approach requires a continuous availability and a track-selective accuracy. A probabilistic approach is followed up in order to cope with multiple sensors, measurement errors, imprecise information, and hidden variables as the topological position within the track network. The nonlinear estimation of the train localization posterior is addressed with a novel Rao-Blackwellized particle filter (RBPF) approach. There, embedded Kalman filters estimate certain linear state variables while the particle distribution can cope with the nonlinear cases of parallel tracks and switch scenarios. The train localization algorithmis further based on a trackmap andmeasurements froma GlobalNavigation Satellite System(GNSS) receiver and an inertial measurement unit (IMU). The GNSS integration is loosely coupled and the IMU integration is achieved without the common strapdown approach and suitable for low-cost IMUs.The implementation is evaluated with realmeasurements from a regional train at regular passenger service over 230 km of tracks with 107 split switches and parallel track scenarios of 58.5 km.The approach is analyzed with labeled data by means of ground truth of the traveled switch way. Track selectivity results reach 99.3% over parallel track scenarios and 97.2% of correctly resolved switch ways

Experimental investigation on the use of multiple very low-cost inertial-based devices for comfort assessment and rail track monitoring

The periodic rail track inspection is mandatory to ensure ride comfort and operational safety. However, conventional monitoring technologies have high costs, stimulating research on low-cost alternatives. In this regard, this paper presents the first experimental results on the use of multiple very low-cost sensors aboard trains for vibration monitoring, proposing a collective approach to provide more accurate and robust results. Nine devices comprising commercial-grade inertial sensors were tested in different distributions aboard a high-speed track recording train. Frequency weighted accelerations were calculated in accordance with ISO 2631 standard as comfort and indirect track quality index. As expected, vertical and lateral results were correlated with, respectively, track longitudinal level (range D1, maximum correlation coefficient of 0.86) and alignment (range D2, maximum correlation coefficient of 0.60), with numerically similar results when considering the fused signal. The collective approach's potential was proven as a result of the noise reduction and the discrepant sensor identification

Train Track Misalignment Detection System

A feasible, portable and low-cost detection technique for train track misalignment was proposed. Currently, the detection of orientation movement of train along a flat head rail focuses on using different combination of optical sensor, accelerometer and gyro sensors, separated at several compartment and parts of the train. However, due to high implementation cost and complexity, these systems could not be widely implemented in all of the passenger-loaded compartments train and not suitable to switch from one platform to another, as it requires complex mounted installations. Hence, a MEMS-based Inertia Measurement Unit (IMU) was proposed to be implemented as an alternative low-cost and portable detection solution. The primary objective focuses on identifying potential misaligned track section through tri-axis Euler angles and tri-axis acceleration of the train. Equipped with an onboard Arduino ATMega328 microcontroller, the IMU was programmed through Arduino IDE by using USB-to-UART converter. Direction-cosine-matrix (DCM) algorithm was also implemented to detect and correct numerical error for the gyroscope via reference data from accelerometer. Practical implementation had also being conducted on both car and passenger-loaded train. These data were extracted onto PC for storage and post-processing via MATLAB. The measurements were analyzed and presented with discussion on track characteristics, train motion and noise. Also, analysis through the frequency spectrum over time provides insight onto possible misalignment region. The overall measurement analysis showed good correlation between actual track features and IMU sensor data

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

This article illustrates some outcomes of the EU project Assets4Rail, founded within the Shift2Rail Joint Undertaking. Nowadays, Track recording vehicles (TRV) are equipped with laser/optical systems with inertial units to monitor track geometry (TG). Dedicated trains and sophisticated measurement equipment are difficult, costly to acquire and maintain. So the time interval between two TRV recordings of the TG on the same line section cannot be too close (twice per month to twice per year). Recently, infrastructure managers have been more interested in using 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 detecting the vertical irregularity is a straightforward method by doubling the recorded acceleration, it is yet an unsolved issue for lateral irregularities due to the complicated relative wheel-rail motion. The proposed system combines wheel-rail transversal relative position data with on-board lateral acceleration sensors to detect lateral alignment issues. It includes a functional prototype of an on-board computer vision sensor capable of monitoring Lateral displacement for TG measurements. This eliminates measurement errors due to wheelset transverse displacements relative to the track, which is essential for calculating lateral alignment. The sensor system prototype was tested in Italy at 100 km/h on the Aldebaran 2.0 TRV of RFI, the main Italian Infrastructure Manager. It was found that the estimated lateral displacement well corresponds to the lateral alignment acquired by the Aldebaran 2.0 commercial TG inspection equipment. Moreover, due to the lack of measurement of the acceleration on board the Aldebaran 2.0 TRV, a Simpack® simulation provide with axle box acceleration values, to evaluate the correlation between them, LDWR and track alignment issues

Developing TRACKER - Portable Monitoring System using Kalman Filtering to Track Rotational Movement of Bridges

The combined effects of flooding and scour are the primary causes of bridge failure over flowing water. Improvements in structural health monitoring and inertial sensors have led to the development of advanced monitoring systems that can provide bridge owners with detailed information on the performance of the structure and allow informed decisions to be made about time-critical safety issues following a storm event. However, such systems remain prohibitively expensive for the majority of smaller structures which make up the wider transport network. This thesis details the development of a robust, portable data acquisition logger (TRACK ER), which can be used to target vulnerable infrastructure during a storm event to increase the resilience of the wider transport network. TRACKER uses condition monitoring, recording quasi-static and dynamic deformations, to track the performance of a bridge under the combined effects of storm loading. A benefit of this method is that it requires no direct input force or prior knowledge of the bridge model. Traditionally, tiltmeters or accelerometers are used to measure rotation for structural health monitoring purposes but such sensors can struggle to isolate rotation from translational acceleration if the structure is linearly accelerating. Gyroscopes offer improved rotational measurement capabilities but gyroscope measurements are known to drift over time as a result of the iterative process of converting rate gyroscope data. This thesis will explore gyroscopes as a complementary sensor to accelerometers and introduce a Kalman filter that combines both inertial sensors measurement data to obtain optimised rotation data. To improve the performance of the Kalman filter, the filter is adapted to automatically update the process and noise measurement values. TRACKER, a robust, portable data acquisition logger, was developed and equipped with inertial sensors to provide a stand-alone system that can be rapidly deployed to target vulnerable infrastructure. Verification of the new logger was performed under controlled laboratory conditions to prove the validity of the new logger. The rotational data showed good agreement with rotational measurements obtained from an industry gold-standard vision-based measurement system. TRACKER was deployed on a variety of in-service bridges using different loading scenarios to demonstrate the ability of the new logging system, including loading from ambient weather conditions. TRACKER successfully tracked the performance of the structures, proving the ability of the logger to track the quasi-static and dynamic deformations of a structure during loading from traffic and environmental conditions

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

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

The main objective of this paper is to assess the feasibility and accuracy of inferring key track condition parameters, e.g., vertical alignment and horizontal alignment of the rails, using onboard micro-electro-mechanical-system (MEMS) accelerometers. To achieve this aim, a prototype of an onboard data acquisition system (DAQ) 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. Moreover, it was found that two vertical bogie accelerometers (left and right side) 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 two measurement axes (vertical and lateral) are an effective system and can provide quantitative estimates of vertical alignment and trends in the geographical distribution of horizontal alignment