US20070246612A1 - Processing of Railway Track Data - Google Patents

Processing of Railway Track Data Download PDF

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Publication number
US20070246612A1
US20070246612A1 US11/628,311 US62831105A US2007246612A1 US 20070246612 A1 US20070246612 A1 US 20070246612A1 US 62831105 A US62831105 A US 62831105A US 2007246612 A1 US2007246612 A1 US 2007246612A1
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data
sample
transfer function
track
stored
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US11/628,311
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Sandor Patko
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DeltaRail Group Ltd
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DeltaRail Group Ltd
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Assigned to DELTARAIL GROUP LIMITED reassignment DELTARAIL GROUP LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: PATKO, SANDOR MATYAS
Publication of US20070246612A1 publication Critical patent/US20070246612A1/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
    • B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
    • B61K9/08—Measuring installations for surveying permanent way

Definitions

  • This invention relates to an apparatus and a method for processing data, in particular data obtained by monitoring a railway track, such data for example being used for assessing the quality of the track.
  • Track recording vehicles are known, which are used in surveying a railway track to provide data representing the undulations of the rails in the vertical and horizontal planes, and their curvature.
  • Software packages are also available, for example a software product under the trade mark VAMPIRE (from AEA Technology plc), for predicting how a particular vehicle will respond when travelling at a particular speed along a track; such software packages, which may be referred to as vehicle dynamics simulations, require input data providing an undistorted representation of the track.
  • VAMPIRE from AEA Technology plc
  • vehicle dynamics simulations require input data providing an undistorted representation of the track.
  • the raw data obtained by the sensors on a track recording vehicle provide information about train movement, and can be processed to determine track data, in particular being filtered to distinguish between short wavelength data and long wavelength data. This filtration process may introduce phase differences.
  • a method of obtaining data on the quality of a railway track comprising:
  • the multiplicity (N) is an odd number; and preferably the impulse function is centred on the middle sample of those stored, that is ((N+1)/2) th sample if N is odd.
  • the impulse function need not be a symmetrical function; it is ‘centred’ in the sense that it is a function not of absolute time (or distance) but of the time (or distance) relative to that of a specific stored sample.
  • the method described above enables a series of output data samples to be generated substantially in real-time, the only delay being that taken for the receipt of ((N+1)/2) samples.
  • This method may be performed within a track recording vehicle. For example it can enable amplitude and phase distortions of track geometry signals to be removed, so that the corrected signals can be used as input for a vehicle dynamics simulation. Another application is that, once amplitude and phase distortions of track geometry signals have been removed, the signals correctly represent the shape of track features such as dipped rail joints, and so can be used to guide track maintenance.
  • the method of the invention can also remove distortions due to anti-aliasing filters.
  • the present invention also provides an apparatus for performing this method.
  • the method of the invention may be used to provide input data to a vehicle dynamics simulator carried in a track recording vehicle, so that the simulator can deduce the risk of derailment of a particular type of vehicle in substantially real-time.
  • the vehicle dynamics simulator could give a warning signal if the corresponding simulated vehicle would be derailed.
  • the track survey vehicle can, substantially in real-time, provide warnings of track sections that would give high derailment risk for a particular type of vehicle at a particular speed.
  • Warnings might also be given if the simulated vehicle would subject passengers to unacceptable jolts, or if the simulated vehicle would subject the portion of track to unacceptable track forces, and such information could also be reported as soon as the vehicle has passed over that section of the track. This enables track maintenance to be targeted at those sections of track most in need of improvement.
  • an apparatus incorporating the present invention is installed in a track recording vehicle 10 , that is to say a rail vehicle incorporating transducers monitoring displacements and accelerations of the bogie and/or the body as the vehicle 10 moves along the track 11 .
  • a track recording vehicle 10 that is to say a rail vehicle incorporating transducers monitoring displacements and accelerations of the bogie and/or the body as the vehicle 10 moves along the track 11 .
  • transducers monitoring displacements and accelerations of the bogie and/or the body as the vehicle 10 moves along the track 11 .
  • it might incorporate an accelerometer monitoring vertical accelerations of the bogie, and a displacement transducer monitoring vertical displacement of the axle relative to the bogie; data from such transducers would enable undulations in the vertical plane of each rail of the track to be monitored.
  • accelerometers measuring horizontal accelerations, along with a displacement transducer to monitor the wheel relative to the bogie enable undulations of the track in the horizontal plane to be monitored.
  • Track recording vehicles normally incorporate several different transducers, data from the transducers being sampled every 1 ⁇ 8 m and digitized, and the output data may involve calculations that combine data from several such transducers.
  • the data is subjected to signal processing (represented diagrammatically by box 12 ) that includes filtration so as to generate track data, which would typically be displayed to an operator, for example using a graphical interface, and stored for subsequent processing.
  • the data may also be stored in conjunction with data from other sensors, for example positional data from a GPS sensor.
  • the data typically would represent alignment (a measure of the offset of the rails from the required smooth curve, measured in mm), and curvature (indicating the reciprocal of the radius of the curve followed by the track, measured in km ⁇ 1 ).
  • the cutoff wavelength is set at 70 m, horizontal displacements of shorter wavelength than this being treated as alignment, and horizontal displacements of longer wavelength being treated as curvature.
  • the data typically would represent “top” (a measure of the displacement of the rails from the required smooth curve, measured in mm), and gradient (indicating the slope of the track, in mm/mm).
  • the cutoff wavelength in this case is typically also set to 70 m.
  • the track data streams from the processor 12 representing alignment, curvature, and top (and possibly also gradient), and possibly other data streams such as positional information, are transmitted to a data post-processing server 14 , and thence to a reporting server 16 , and so to various display interfaces 18 and to a data store 20 .
  • Data streams representing alignment, curvature, and top (and possibly also gradient) are also supplied by the post-processing server 14 to several different vehicle dynamics modules 22 (three such modules are represented).
  • Each such module 22 consists of a microprocessor arranged to model the dynamics of a particular vehicle travelling along the track 11 at a particular speed.
  • the output of these vehicle dynamics modules 22 is fed back to the data post-processing server 14 , and is supplied to the reporting server 16 along with the corresponding track data (processed as described below).
  • the data post-processing server 14 is programmed to subject the track data streams from the processor 12 to the filtration process of the invention.
  • the processor 12 is used to separate high frequency (short wavelength) components from low frequency (long wavelength) components.
  • Analogue filters or digital infinite impulse response (IIR) filters can perform these tasks efficiently, but they introduce distortion. Methods are known to eliminate this phase distortion, either avoiding it by using finite impulse response (FIR) filters instead of IIR filters, or by back filtering the already distorted data with an identical IIR filter to restore the original phase content.
  • FIR finite impulse response
  • the server 14 performs signal shaping of the incoming data, and forwards it to the rest of the system for storage and/or further processing.
  • the signal processing method can deal with both spatially and temporally sampled data streams. It can also perform ‘cross-domain’ operations, as well, that is to say to perform temporally defined operations in spatially sampled (taken at equal distances) data, and vice versa.
  • the server 14 consists of:
  • a buffer memory to store N samples of the data stream, including the measured value and a time or distance stamp, indicating the time or distance the measurement was taken.
  • the type of the stamp data depends on the actual operation: if temporal operation is needed, then time stamp, if spatial operation is needed the distance stamp has to be attached to each measured value.
  • the actual sampling method does not affect the operation of the filter. For example, usually the measurements are taken at equal distances, so if the vehicle speed is increasing, then the differences between the consecutive time stamps will decrease, but the system operation will not change.
  • the details such as the data transfer protocols, memory type etc. must be adjusted to the system in which the server 14 is used. In certain cases it may be a separate instrument connected to the data bus of the measurement system, in other cases it may be fully integrated into the measurement system.
  • the operation of the server 14 is as follows:
  • the samples of the incoming data are stored in an N-element first-in-first-out (FIFO) buffer, which is initialized with zeros as measured values. Each new sample enters the first slot of the buffer, moving the previous measurements one slot forward. The data that had been in the N th slot is deleted, since it is replaced by the one coming from the (N ⁇ 1) th slot. N is preferably odd. 2.
  • FIFO first-in-first-out
  • Y(T 0 ) is the output data, time stamped as taken at T 0 .
  • T 0 is the actual time stamp of the ((N+1)/2) th data in the buffer. In a certain sense, the calculation above is centred on T 0 , and the output data stream is always delayed by (N+1)/2 samples.
  • T 1 is the time stamp of the oldest (N th ) data in the buffer.
  • T 2 is the time stamp of the latest (1 st ) data in the buffer. It is also true, that T 1 ⁇ T 0 ⁇ T 2 .
  • X(t) is the data stream stored in the buffer.
  • F(t) is the finite impulse function, derived from the desired restoration.
  • F(t) is integratable between any possible t values.
  • Equation 1 which is expressed above as an integral (implying continuous functions), must in practice be performed as a summation, by a suitable discrete calculation method. Since each sample is processed separately, and has an associated time stamp, if the time intervals or spatial distances between successive samples vary, or there are randomly missing samples, overall operation is not affected. This is a significant advantage.
  • Eq. 1 is shown in the temporal domain.
  • the calculated output is forwarded for further processing.
  • the impulse function is defined from the desired system behaviour, described by a transfer function. Transfer functions are complex equations that describe the system behaviour as a function of the cyclic frequency, ⁇ . If H(j ⁇ ) is the transfer function of a filter, then:
  • the selected transfer function H T is one that reverses at least the phase change, and may also be selected so as to return the amplitude to its original value.
  • F(t) ⁇ - ⁇ ⁇ ⁇ F ⁇ ( t ) ⁇ e j ⁇ ⁇ ⁇ t ⁇ d t Eq . ⁇ 2
  • the final step is to define the size of the buffer memory.
  • T l and T h such that the following approximation will be true:
  • H T ⁇ ( j ⁇ ) ⁇ - ⁇ ⁇ ⁇ F ⁇ ( t ) ⁇ e j ⁇ ⁇ ⁇ t ⁇ d t ⁇ ⁇ T 1 T h ⁇ F ⁇ ( t ) ⁇ e j ⁇ ⁇ ⁇ t ⁇ d t Eq . ⁇ 3
  • a track recording vehicle 10 will include various transducers which measure aspects of the vehicle movement, such as an accelerometer, gyroscope etc.
  • the signal from such a transducer which is an analogue signal
  • an anti-aliasing filter is low frequency pass analogue filters, eliminating the undesired frequency content.
  • the data processor 12 would then produce digital output signals by sampling the analogue signal at equal distances along the track.
  • Anti-aliasing is essential, but it introduces a non-linear phase delay of the incoming signal. This phase delay will distort the shape of the signal.
  • back-filtering was only the way to restore the original phase content. However, back-filtering changes the amplitudes in the transition band and cannot be used if the results are needed in real time.
  • , and ⁇ ( ⁇ ) ⁇ ( H ( j ⁇ )), Eq. 4 where ⁇ is in radians per second. These two functions can be analytically derived, or measured.
  • H T which leaves the amplitude intact, but reverses the phase delay.
  • F(t) and N can be calculated as described above. Once these have been calculated, the server 14 can restore the original phase content of the incoming signal.
  • the track curvature is split into long and short wavelength parts: the long wavelength part describes the track design, all the bends and straight sections needed to lead the train from A to B, while the short wavelength part describes the local deviations from the design, affecting the ride quality along the track.
  • curvature It is difficult to measure curvature directly, so different indirect methods are used.
  • One of them is asymmetric versine; the asymmetric versine, v, is measured by considering a fixed length chord between two points on the rail. The chord is divided by a point Y into two unequal parts, L 1 and L 2 , and v is the distance of the rail from the point Y measured along a line perpendicular to the chord.
  • Asymmetric versine is easy to measure both manually and automatically. It gives a broadband description of the lateral track geometry, recording both short and long wavelengths components in the same output.
  • a complicated transfer function is required, which also introduces phase distortion.
  • Previously-known methods were unable to give a proper reconstruction of curvature from versine in real time.
  • the server 14 can be configured to reproduce broadband curvature from digital asymmetric versine input in real time.
  • H CV ⁇ ( j ⁇ ) 1 ⁇ 2 ⁇ ( 1 - L 2 L 1 + L 2 ⁇ e - j ⁇ ⁇ L 1 ⁇ ⁇ - L 1 L 1 + L 2 ⁇ e j ⁇ ⁇ L 2 ⁇ ⁇ ) Eq . ⁇ 8 where ⁇ is in radians per metre, and L 1 and L 2 are in metres.
  • a track recording vehicle 10 might include several such vehicle dynamics modules 22 operating in parallel, for example twelve rather than the three modules 22 shown here. Operation of this one vehicle 10 is therefore equivalent to running a fleet of a dozen different vehicles that may use this particular route, each at their own speed, and each of the virtual vehicles is effectively instrumented for assessing the risk of derailment, and also other parameters such as passenger comfort, track forces, vehicle kinematic movements etc.
  • This information is obtained in real-time, and is reported as part of the data provided to the display interfaces 18 as soon as the track recording vehicle 10 has passed over a portion of the track 11 .
  • the information is embedded in the same stream of data as the information on track geometry. Hence it can be readily interfaced to track management software.
US11/628,311 2004-06-02 2005-04-28 Processing of Railway Track Data Abandoned US20070246612A1 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
GBGB0412215.6A GB0412215D0 (en) 2004-06-02 2004-06-02 Processing of railway track data
GB0412215.6 2004-06-02
PCT/GB2005/001600 WO2005118366A1 (fr) 2004-06-02 2005-04-28 Traitement de donnees de voies ferrees

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US (1) US20070246612A1 (fr)
EP (1) EP1771327A1 (fr)
AU (1) AU2005249761A1 (fr)
CA (1) CA2573435A1 (fr)
GB (1) GB0412215D0 (fr)
WO (1) WO2005118366A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090094848A1 (en) * 2006-01-31 2009-04-16 Deltarail Group Limited Track Twist Monitoring
CN102353717A (zh) * 2011-06-28 2012-02-15 哈尔滨工业大学 基于非负张量分解振动特征的钢轨伤损探测装置和方法
US11014587B2 (en) * 2017-03-27 2021-05-25 Harsco Technologies LLC Track geometry measurement system with inertial measurement
JP2021530407A (ja) * 2018-07-11 2021-11-11 プラッサー ウント トイラー エクスポート フォン バーンバウマシーネン ゲゼルシャフト ミット ベシュレンクテル ハフツングPlasser & Theurer, Export von Bahnbaumaschinen, Gesellschaft m.b.H. 軌道区間を監視する方法およびシステム

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9663127B2 (en) 2014-10-28 2017-05-30 Smartdrive Systems, Inc. Rail vehicle event detection and recording system
US9487222B2 (en) 2015-01-08 2016-11-08 Smartdrive Systems, Inc. System and method for aggregation display and analysis of rail vehicle event information
US9902410B2 (en) 2015-01-08 2018-02-27 Smartdrive Systems, Inc. System and method for synthesizing rail vehicle event information
US9296401B1 (en) 2015-01-12 2016-03-29 Smartdrive Systems, Inc. Rail vehicle event triggering system and method

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US20010045130A1 (en) * 1998-02-24 2001-11-29 Massachusetts Institute Of Technology Flaw detection system using acoustic doppler effect
US6904087B2 (en) * 2002-09-17 2005-06-07 Via Technologies, Inc. Adaptive multi-modulus algorithm method for blind equalization

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DE4429517C2 (de) * 1994-08-19 1996-08-14 Man Technologie Gmbh Vorrichtung und Verfahren zur Korrektur einer Meßkurve oder eines Signalverlaufs und deren bzw. dessen Anwendung zur Rekonstruktion von Lagefehlern bei Bahngleisen aus geometrischen Relativmessungen
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US4760797A (en) * 1985-02-20 1988-08-02 Southern Railway Company Method and apparatus for automated tie detection and tamping
US5358202A (en) * 1992-07-21 1994-10-25 Consolidated Rail Corporation Cab signal track code analyzer system
US5768317A (en) * 1995-05-08 1998-06-16 National Semiconductor Corporation Equalization filter compensating for distortion in a surface acoustic wave device
US5923422A (en) * 1997-02-07 1999-07-13 Bruker Analtyik Gmbh Method of obtaining an optical FT spectrum
US20010045130A1 (en) * 1998-02-24 2001-11-29 Massachusetts Institute Of Technology Flaw detection system using acoustic doppler effect
US20010019263A1 (en) * 1999-03-17 2001-09-06 Hegeon Kwun Magnetostrictive sensor rail inspection system
US20010033612A1 (en) * 2000-03-07 2001-10-25 Miguel Peeters Method to determine a channel characteristic, and discrete wavelet transmitter and receiver to perform the method
US6904087B2 (en) * 2002-09-17 2005-06-07 Via Technologies, Inc. Adaptive multi-modulus algorithm method for blind equalization

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090094848A1 (en) * 2006-01-31 2009-04-16 Deltarail Group Limited Track Twist Monitoring
CN102353717A (zh) * 2011-06-28 2012-02-15 哈尔滨工业大学 基于非负张量分解振动特征的钢轨伤损探测装置和方法
US11014587B2 (en) * 2017-03-27 2021-05-25 Harsco Technologies LLC Track geometry measurement system with inertial measurement
JP2021530407A (ja) * 2018-07-11 2021-11-11 プラッサー ウント トイラー エクスポート フォン バーンバウマシーネン ゲゼルシャフト ミット ベシュレンクテル ハフツングPlasser & Theurer, Export von Bahnbaumaschinen, Gesellschaft m.b.H. 軌道区間を監視する方法およびシステム
JP7411655B2 (ja) 2018-07-11 2024-01-11 プラッサー ウント トイラー エクスポート フォン バーンバウマシーネン ゲゼルシャフト ミット ベシュレンクテル ハフツング 軌道区間を監視する方法およびシステム

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Publication number Publication date
WO2005118366A1 (fr) 2005-12-15
GB0412215D0 (en) 2004-07-07
AU2005249761A1 (en) 2005-12-15
CA2573435A1 (fr) 2005-12-15
EP1771327A1 (fr) 2007-04-11

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