Railway track irregularity and curvature estimation using doppler LIDAR fiber optics

2016 ◽  
Vol 232 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Masood Taheri Andani ◽  
Andrew Peterson ◽  
Josh Munoz ◽  
Mehdi Ahmadian
Keyword(s):  
Fiber Optics ◽  
Field Testing ◽  
Railway Track ◽  
Vehicle Speed ◽  
Doppler Lidar ◽  
Close Match ◽  
Track Geometry ◽  
Lidar Measurements ◽  
Curvature Estimation ◽  
Geometry Measurement

The application of Doppler-based LIght Detection and Ranging (LIDAR) technology for determining track curvature and lateral irregularities, including alignment and gage variation, are investigated. The proposed method uses track measurements by two low-elevation, slightly tilted LIDAR sensors nominally pointed at the rail gage face on each track. The Doppler LIDAR lenses are installed with a slight forward angle to measure track speed in both longitudinal and lateral directions. The lateral speed measurements are processed for assessing the track gage and alignment variations, using a method that is based on the frequency bandwidth dissimilarities between the vehicle speed and track geometry irregularity. Using the results from an extensive series of tests with a body-mounted Doppler LIDAR system on-board a track geometry measurement railcar, the study indicates a close match between the LIDAR measurements and those made with existing sensors on-board the railcar. The field testing conducted during this study indicates that LIDAR sensors could provide a reliable, non-contact track monitoring instrument for field use in various weather and track conditions, potentially in a semi-autonomous or autonomous manner.

Novel Algorithm for Mutual Alignment of Railway Track Geometry Measurements

2021 ◽  
pp. 036119812110319
Author(s):  
Kristin Eklöf ◽  
Andrew Nwichi-Holdsworth ◽  
Johan Eklöf
Keyword(s):  
Measurement Errors ◽  
Measurement Data ◽  
Weighted Average ◽  
Railway Track ◽  
Railway Network ◽  
Track Geometry ◽  
Spatially Correlated ◽  
Geometry Measurement ◽  
Variable Sample Rate ◽  
Novel Algorithm

Track geometry measurements are regularly collected to monitor the condition of a railway network. To detect deterioration patterns and enable predictive maintenance, sequential measurement runs must be mutually aligned which has been proven a serious challenge. This paper presents a novel algorithm for mutual alignment of track geometry signal data. It resolves several previously intractable alignment problems: highly segmented data with variable sample rate, spatially correlated and uncorrelated measurement errors, convergence to true locations, and consistency over time. The algorithm adjusts spatial measurement errors by splitting signals in continuous segments. Re-sampled, error-corrected signals are mutually aligned using cross correlation, and this process is repeated until the mutual alignment meets a pre-defined precision threshold. Missing measurement values are handled by imputing an interpolated offset from nearby segments, ensuring that the signals remain continuous. By using weighted average offsets over all aligned signals, the law of large numbers guarantees convergence and consistency. The practical feasibility of the algorithm is demonstrated on empirical track geometry measurement data from the British railway network, owned and operated by Network Rail.

scholarly journals Identification of Temperature-Induced Deformation for HSR Slab Track Using Track Geometry Measurement Data

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5446 ◽  
Author(s):  
Zai-Wei Li ◽  
Xiao-Zhou Liu ◽  
Yue-Lei He
Keyword(s):  
High Speed ◽  
Detection Method ◽  
Measurement Data ◽  
Railway Track ◽  
Discrete Wavelet ◽  
High Speed Rail ◽  
Track Geometry ◽  
Slab Track ◽  
Geometry Measurement ◽  
Inspection Data

Slab track is widely used in many newly built high-speed rail (HSR) lines as it offers many advantages over ballasted tracks. However, in actual operation, slab tracks are subjected to operational and environmental factors, and structural damages are frequently reported. One of the most critical problems is temperature-induced slab-warping deformation (SWD) which can jeopardize the safety of train operation. This paper proposes an automatic slab deformation detection method in light of the track geometry measurement data, which are collected by high-speed track geometry car (HSTGC). The characteristic of track vertical irregularity is first analyzed in both time and frequency domain, and the feature of slab-warping phenomenon is observed. To quantify the severity of SWD, a slab-warping index (SWI) is established based on warping-sensitive feature extraction using discrete wavelet transform (DWT). The performance of the proposed algorithm is verified against visual inspection recorded on four sections of China HSR line, which are constructed with the China Railway Track System II (CRTSII) slab track. The results show that among the 24,806 slabs being assessed, over 94% of the slabs with warping deformation can be successfully identified by the proposed detection method. This study is expected to provide guidance for efficiently detecting and locating slab track defects, taking advantage of the massive track inspection data.

scholarly journals Analysis of railway track geometry measurement methods usability

2018 ◽  
Vol 2018 (8) ◽  
pp. 40-50
Author(s):  
Waldemar Odziemczyk ◽  
Marek Woźniak
Keyword(s):  
Direct Measurement ◽  
Method Development ◽  
Measurement Methods ◽  
Railway Track ◽  
Data Types ◽  
Track Geometry ◽  
Geodetic Measurements ◽  
Accuracy Comparison ◽  
Geometrical Data ◽  
Geometry Measurement

Precise information of railway tracks geometry is necessary to design alignment project. Geodetic measurements are the most common method of determining this information and sags of arch direct measurement are the traditional and still popular measurement method. Development of geodetic measurements techniques made possible to use another methods such as tacheometry, GNSS, and new methods based on mobile measurement devices. Series of experiments were conducted to set the practical usability of selected modern measurement methods to design track alignment project. The experimental measurements were performed on the 3 km long two-track railway fragment. Following methods were used during the test measurements: sags of arch direct measurement, tacheometry with total station, GNSS, automated methods with the use of a trolley system. Above mentioned measurement methods were compared taking into account time and labour consumption, range of geometric data, measurement equipment cost, reliability and accuracy of surveying procedure. Because of different data types are delivered with various methods, sags of arch were used for comparison of accuracy. Comparison of geometrical data obtained with analysed methods allowed to formulate conclusions concerning practical usability those methods for track alignment project development.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

Dual-Doppler Lidar Measurements for Improving Dispersion Models

2005 ◽  
Vol 86 (6) ◽  
pp. 825-838 ◽  
Author(s):  
Chris G. Collier ◽  
Fay Davies ◽  
Karen E. Bozier ◽  
Anthony R. Holt ◽  
Doug R. Middleton ◽  
...  
Keyword(s):  
Doppler Lidar ◽  
Dispersion Models ◽  
Lidar Measurements

Optimizing Tamper Efficiency Through the Integration of Inertial Based Track Geometry Measurement

2017 ◽  
Author(s):  
Joseph W. Palese ◽  
Sergio DiVentura ◽  
Ken Hill ◽  
Peter Maurice
Keyword(s):  
Time Perspective ◽  
Cloud Service ◽  
New Method ◽  
Maximum Efficiency ◽  
Track Geometry ◽  
Track Maintenance ◽  
Measurement Systems ◽  
Recorded Data ◽  
Available Technology ◽  
Geometry Measurement

Maintaining track geometry is key to the safe and efficient operations of a railroad. Failure to properly maintain geometry can lead to costly track structure failures or even more costly derailments. Currently, there exists a number of different methods for measuring track geometry and then if required, maintaining the track to return track geometry to specified levels of acceptance. Because of this need to have proper track geometry, tampers are one of the most common pieces of maintenance equipment in a railroad operation’s fleet. It is therefore paramount from both a cost and track time perspective to gain maximum efficiency from any one particular tamper. Track geometry is typically measured through a variety of contact and non-contact measurement systems which can mount on a variety of different platforms. With respect to a tamper, a push buggy projector system is typically used to measure track geometry, utilizing the tamper body as the basis for the reference system, Track geometry can be measured utilizing this technology during a prerecording run. Then, the software onboard the tamper analyzes the recorded data to determine the best fit and calculate throws that achieve a better track alignment, particularly in curves. During the tamping operation, the tamper buggy system and frame adjust the track. Due to its design, track geometry measurements can only be made at low speed (roughly 4mph) which can severely affect the efficiency of the tamper. To help decrease pre maintenance inspection times, an inertial based track geometry measurement system has been developed and integrated into the tamper’s operating software. This system can mount directly to the frame of a tamper and operate at hy-rail to very low speeds. Measurements made can be fed directly into the tamper control system to guide where and how track geometry adjustments need to be made. In addition, the capability to collect data during travel mode without the buggies extended allows for the collection of data at any time. Thus, data can be recorded when traveling back and forth to a stabling location, before and/or after grinding. This allows for synchronization of data at a later time to utilize for adjusting the track. Also, data can be collected post-work to allow for the comparison of pre and post geometry to allow for the determination of the effectiveness of a given tamping operation. Tampers equipped with this track geometry system facilitate the foundation for an enterprise solution. Data that is measured and collected can be sent to a cloud service, in real time that will provide exception reports, health status, and rail health trend analyses. Utilizing the available technology further optimizes response time in track maintenance. This paper will introduce this new method of mounting and completely integrating an inertial based track geometry system onto a tamper. In addition, studies will be presented which confirm the ability of this system to replicate the tamper’s projection based track geometry system. Finally, a comprehensive study on efficiency gains will be presented directly comparing a standard method of maintaining a segment via a tamper to this new method of using onboard inertial track geometry measurement.

Application of LIDAR technology for rail surface monitoring and quality indexing

2017 ◽  
Vol 232 (5) ◽  
pp. 1398-1406 ◽  
Author(s):  
Masood Taheri Andani ◽  
Abdullah Mohammed ◽  
Ashish Jain ◽  
Mehdi Ahmadian
Keyword(s):  
Doppler Frequency ◽  
Light Detection And Ranging ◽  
Doppler Lidar ◽  
Frequency Range ◽  
Absorption Properties ◽  
Surface Conditions ◽  
Lidar Measurements ◽  
Rail Inspection ◽  
Surface Monitoring ◽  
Surface Figure

This paper investigates the application of Doppler Light Detection and Ranging (LIDAR) sensors for the assessment of the top of rail lubricity condition and layer material. Different top of rail conditions are distinguished by the system using a new pair of rail surface indices defined based on LIDAR measurements. These indices provide quantitative representations of the top of rail condition due to the fact that Doppler frequency range and spectral magnitude of a backscattered LIDAR beam are functions of the rail surface figure as well as the light absorption properties of the surface material. Laboratory tests are conducted to demonstrate the feasibility of the proposed top of rail indexing operation. The results indicate that LIDAR sensors are capable of detecting and distinguishing between different top of rail surface conditions. Instrumenting rail inspection vehicles with Doppler LIDAR systems reduces reliance on empirical top of rail lubricity and surface assessments (such as observing the sheen of the rail or tactilely sensing various residues on the rail), in favor of reliable and repeatable measurements.

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