Energy harvesting from railway slab-tracks with continuous slabs

This paper contributes to the literature and development of knowledge in the topic of energy harvesting by presenting the modelling and calculations of energy from vibration of railway tracks due to moving trains on floating-slab tracks with continuous slabs, considering both the quasi-static and dynamic effects. The floating-slab track is modelled as a double Euler–Bernoulli beam connected by continuous spring and damper elements. The dynamic excitation is accounted for by considering the un-sprung axles of a passing train with a number of coaches. The dynamic excitation is simulated using randomly generated unevenness from standard functions of power spectral density . The responses of rails’ beam and slab are calculated for different unevenness realizations, and then used as inputs for a base-excited single-degree-of-freedom system that models the harvester. The change in the harvested energy is investigated due to the change of natural frequency of the harvester, the change of condition of track and change of train’s velocity. The parameters used in this paper correspond to tracks and trains for Doha metro and unevenness information from the literature. The results show that more energy can be harvested by tuning the harvester’s natural frequency to the frequency of axle-track resonance. It is found that a maximum mean-energy can be harvested from the rails of 0.35 J/kg for a train moving at 100 km/h for a track with poor condition and this is obtained at the axle-track resonance frequency. For the same track condition, a reduction of about 55% and 61% is observed for train’s velocities of 70 km/h and 40 km/h, respectively. Using a track with medium and good conditions resulted in reduction of the mean harvested energy at the axle-track resonance by 73.5% and 99.9%, respectively.

Acceleration Anomaly Detection Method for Sensor Axle Box of Unmanned Vehicle

Detecting the anomaly acceleration of the sensor’s axle box of unmanned vehicles is very important for judging the wear condition of vehicle track and evaluating the state of the track. A capacitive accelerometer is connected with acquisition equipment to collect the information of train axle box’s acceleration change when the vehicle is running; instrument amplifier AD8250 with a digitally programmable gain is selected as system signal conversion chip to realize acceleration signal conversion; sliding variance of axle box’s acceleration of the unmanned vehicle is calculated based on sliding variance statistical analysis method, which is confirmed by time window and distance window. Fixing the width of a sliding window according to the response statistics caused by the line excitation link, the acceleration sliding variance is compared with the standard one to determine whether the acceleration is in an anomaly state. The test results show that the anomaly acceleration of the sensor axle box of the unmanned vehicle detected by the proposed method is consistent with the actual results, which provides a reliable basis for vehicle track condition assessment.

Investigation of Dynamic Factor, Rail Stress and Track Foundation Modulus of Ballasted Railway Track for Different Bed Conditions Using Numerical Methods

The track stiffness is the primary function of roadbeds thickness and subgrade characteristics. For this purpose, numerical scale track finite element technique representing the ballasted track with multi layered substructure founded on subgrade was simulated. The track deflection, stress was abstracted in static and dynamic conditions. The track significant design parameters: Foundation modulus, rail fatigue strength, rail bending stress and stress on subgrade levels were evaluated by using improved current track design numerical methods and compared against field test results which were carried out on part of MG Double track high speed main line (1600 km). Mathematical equations were developed to correlate the variables; ballast thickness, settlement, track stiffness, rail bending stress and rail fatigue strength on varying subgrade soil modulus. Incorporation of this parametric study will improve and optimise the conventional track design and maintenance standard. A simple improved track design was introduced by using single track stiffness parameter from conventional plate bearing test (PBT) on Force Displacement (FD) conventional curve method. The improved method with deriving equivalent track stiffness from rail pad and track substructure tested C value are accurate and simple. The current test method to determine the track stiffness in live track condition is expensive and unsafe with operational requirements. This PBT is simple, cost saving on labour, safe and without applying live train load.

A Roadmap for Sustainable Smart Track—Wireless Continuous Monitoring of Railway Track Condition

Ensuring safe train operation, minimizing service interruptions, and optimizing maintenance procedures are primary railway industry focus areas. To support these goals, a multi-disciplinary team of researchers at the University of Illinois at Urbana-Champaign proposed a wireless, continuous, and accurate methodology to monitor track conditions. This project, referred to as “Smart Track”, included the development of a conceptual design plan for efficient and effective implementation of smart monitoring technologies. The project began by establishing guiding research questions, and revising those questions based on track-caused accident data obtained from the Federal Railroad Administration (FRA) and expert opinions from rail experts in the public and private sectors. Next, the research team combined these findings and developed metrics for assigning risk and priorities to various track assets and inspection needs. In parallel, the project team conducted a survey of available wireless technologies for intra-site and site-to-cloud communications. These capabilities were mapped to instrumentation types and requirements (e.g., strain gauges, accelerometers) to ensure compatibility in terms of energy consumption, bandwidth, and communications range. Results identified the rail, crosstie and support, ballast and sub-structure, bridge deck and support, and special trackwork as priority locations for instrumentation. Additionally, IEEE 802.15.4 was found to be the most appropriate cellular communication system within field sites and 4G LTE cellular was determined to be the wireless technology best suited for field site-to-cloud communication. The conceptual design presented in this paper is the first step in achieving the broader goal of Smart Track; to improve the rail industry’s ability to answer critical safety and maintenance-related questions related to the track infrastructure by monitoring and predicting track health.

Assessment of track and turnout condition based on geometry measurement and railhead condition data

The paper presents the procedure of track and turnout geometry condition assessment, taking into account also the deterioration of the rail running surface. Track geometry measurements are made using manual tools, microprocessor-based portable instruments, and geometry cars. Methods of collecting track and turnout geometry data are discussed, and an exemplary equipment design features are presented. Maintaining and possible improvement of the technical condition of the permanent way call for regular inspections providing voluminous data requiring detailed analysis. The approach based on track line-speed dependent geometry parameters analysis is explained. Several synthetic track condition assessment coefficients are described, and analysis of the temporal trend of the track and turnout geometry change. Train operation safety is also affected by changes on the running surface of the rails. In addition to the track geometry, the significant reasons for train operation safety are the railhead wear being affected by the type of transport, traffic intensity and maximum allowable axle load. Determining the permanent way condition with the continuous design and maintenance characteristics is possible if measured on the minimum 200-300 m length with the measurement steps of ca 0.5 m. Comments on employing the Artificial Intelligence tools for track and turnout condition analysis are provided. Most of the inspection data collected using various equipment, like track and turnout geometry measurement data and video inspection information, can be analysed automatically by the dedicated software agents. Such an approach yields analysis results equivalent to the standard inspections, except that the trains and self-propelled trolleys can record data at higher speeds, railways staff could achieve.

Effective Method for Diagnosing Continuous Welded Track Condition Based on Experimental Research

The correct operation of the continuous welded track requires diagnosing its condition and preparation of track metrics requiring measurements of displacements of rail under operation. This is required as there are additional thermal stresses in the rails with values depending on the temperature changes of the rails. Therefore, the climatic conditions are important. This paper presents the original effective analytical method for diagnosing the condition of continuous welded track based on experimental research. The method allows for an appropriate repair or maintenance recommendation. In the experimental research, the authors considered track diagnostic conditions for two conditions: track under load and track without load. This paper presents empirical formulas for calculating rail temperature and longitudinal force based on ambient temperature, developed from long-term measurements. The formulas were developed for a track located on a straight section—both for a rail loaded and unloaded with a passing train under the following conditions: 60E1 rail, not on an engineering structure, conventional surface, wooden sleepers and very high train traffic load. The obtained results in the value of the correlation coefficient R2 ≥ 0.995 attest to very high accuracy of the calculations performed with the method proposed by the authors.

Optimized Railway Track Condition Monitoring and Derailment Prevention System Supported by Cloud Technology

This paper describes an easy way to monitor railway track abnormalities and update information on the track’s status to the cloud. Abnormalities present in railway tracks should be identified promptly and rectified to ensure safe and smooth travel. In this paper, a cloud-based track monitoring system (CTMS) is proposed for the monitoring of track conditions. The micro-electro mechanical systems (MEMS) accelerometers which are mounted in the axle are used to measure the railway track abnormality. The measured signal is optimized using the flower pollination optimization algorithm (FPOA). Because of signaling problems in the global positioning system (GPS), it is difficult to estimate the exact location of the abnormality in real time. A new method is introduced to overcome this problem. It provides the location of an abnormality even when the GPS signal is absent. The performance of the CTMS is compared with three different speed scenarios of the vehicle. The information about the abnormality on the track can be shared with other trains that pass through the same location so that the driver can reduce speed in that location to avoid derailment. Finally, an experimental setup was developed and the performance of CTMS is studied under four different irregularity cases.

Assessment of influence of rail fastening operation on track condition

The paper considers the one of the most important factors that indirectly assesses the serviceability of rail fastening — a number of arising faults in geometry of rail track. On the initial stage the authors have studied sections of railway track with ARS-4, ZhBR-65Sh and Vossloh rail fastening. In result of the study, the authors have got dependencies and made conclusions that characterize the serviceability of railway track on sections with specified types of rail fastening.

Rail track condition monitoring: a review on deep learning approaches

Rail track is a critical component of rail systems. Accidents or interruptions caused by rail track anomalies usually possess severe outcomes. Therefore, rail track condition monitoring is an important task. Over the past decade, deep learning techniques have been rapidly developed and deployed. In the paper, we review the existing literature on applying deep learning to rail track condition monitoring. Potential challenges and opportunities are discussed for the research community to decide on possible directions. Two application cases are presented to illustrate the implementation of deep learning to rail track condition monitoring in practice before we conclude the paper.