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 rolling 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.

The raw data obtained by the sensors on a track recording vehicle provides information about train movement, and is then processed to determine track data, in particular being filtered to distinguish between short wavelength data and long wavelength data, and this filtration process introduces phase differences so that the data is not suitable as input data for such a software package.

Data from such a track recording vehicle is currently subjected to a subsequent filtration process, referred to as"back filtering", to obtain accurate data about the track. However, this process requires all the data about an entire section of track (which might be say 200 km long), and this entire data stream is then processed in reverse. Performing this back filtration process in real-time is clearly impossible.

According to the present invention there is provided a method of assessing the quality of a railway track, the method comprising: a) receiving from a track recording vehicle data that has been subjected to filtration and which comprises at least two data streams representing short wavelength (high frequency) and long wavelength (low frequency) variations of a parameter; b) processing the data streams by a complementary filtration process, combining the high frequency and low frequency data streams, to obtain data representing the variations of that parameter along the track; c) providing this track-representing data to one or a plurality of vehicle dynamics simulators operating in parallel, the simulators being programmed to predict vehicle responses for different vehicles and/or at different speeds; d) providing outputs from the simulators indicating how the or each different vehicle will respond to the track.

Preferably this method is performed within a track recording vehicle.

The present invention also provides an apparatus for performing this method. Preferably the vehicle dynamics simulators comprise microprocessors, all of which receive the same filtered data representing the track, and which process this data in parallel. Where this apparatus is located within a track recording vehicle the output data from each vehicle dynamics simulator may be fed back into

the data stream, so that a data recording device records both the initial track data and the vehicle dynamics simulator data.

The vehicle dynamics simulator preferably provides a warning signal if the corresponding simulated vehicle would be derailed. Hence 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.

Preferably the data from the track recording vehicle includes data about displacements in the lateral plane (and related data about the cross-level or roll), and also data about displacements in the vertical plane.

Preferably, in each case, the data comprises two data streams in relation to each plane. A complementary filtration process is performed to combine the low and high frequency data in relation to the lateral plane; and a second complementary filtration process is performed to combine the low and high frequency data in relation to the vertical plane. Because of the nature of the data from the track recording vehicle, these complementary filtration processes may be different. In particular, low frequency data in the vertical plane may not be available, and the complementary filtration process must be selected accordingly.

The invention will now be further and more particularly described, by way of example only, and with reference to the accompanying drawings which represents as a block diagram the apparatus of the present invention.

The apparatus of the 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. For example 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. Similarly accelerometers measuring horizontal accelerations, along with a displacement transducer to monitor the rail 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. In any event 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.

As regards the lateral plane, 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 followedby the track, which may be measured in km-1). Track gauge, the relative displacement of the two rails, is also derived from the alignment signals.

Typically 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. As regards the vertical plane, 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.

In the apparatus shown, 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 complementary filtration, before the data representing alignment, curvature, top, crosslevel and gauge are provided to the vehicle dynamics modules 22 and indeed to the reporting server 16. The filtration process to which the data streams have been subjected by the processor 12 can be described as a Butterworth filtration.

The characteristic equations for four pole Butterworth filters are: High pass: Low pass: in which p represents the Laplace operator with respect to distance, d/dx, and (Do is 2z/the cutoff wavelength.

The complementary filtration process applies functions A (p) and B (p) to the high frequency and low frequency data streams respectively, so that the resulting data is undistorted. The functions must therefore comply with:

This has a number of solutions: It will be appreciated that such equations must be modified, or the data modified, as in this case the high frequency and low frequency data are related to track position in different ways (e. g. alignment as compared to

curvature ; or top as compared to gradient), and may be in different units. For example data representing alignment, y, may be converted to data representing curvature, K, using the relationship: K = y''/(1 + (yl) 2) 3/2 The selection of one such pair of equations must also take into account the accuracy of the data, as for example integration (represented by the operator 1/p) can cause inaccuracies to build up, so that equations that necessitate repeated integration may not give accurate results. This would for example apply to the first set of equations.

It is thus possible to convert the two streams of data representing lateral displacements (alignment and curvature) into a single stream of data representing lateral displacements (or representing curvature) from which all the phase shifts due to the initial filtration have been eliminated. This corrected single stream of data may be supplied directly to the vehicle dynamics modules 22. Alternatively it may be again split into two streams representing alignment and curvature, this being performed in a way that does not introduce phase shifts, for example by subtracting a running average, and these new data streams (representing alignment and curvature but without phase shifts) then being provided to the vehicle dynamics modules 22.

Data representing the true positions of the rails in the vertical plane, eliminating phase shifts, can be obtained in a similar manner from data representing top and gradient. However in many cases no data representing gradient is provided, so that the phase correction procedure must be modified to take this lack of data into

account. For example the correction may be carried out using the first pair of equations given above, as the coefficient (B) of the missing data is just 1 and can be ignored; to avoid inaccuracies arising from the requirement to perform several successive integrations in function A (which may be referred to as drift) it is desirable to filter the results of the integrations, using a sufficiently long cutoff wavelength that no significant phase differences are introduced. A filter with a cutoff wavelength 200 m or above has been found suitable for this purpose. Alternatively, if the vehicle model used in the simulation (i. e. the vehicle dynamics module 22) has no response to these long wavelengths, it can be fed the unfiltered data, in effect acting as the filter itself.

The resulting corrected data representing short wavelength undulations, i. e. top, is also provided to the vehicle dynamics modules 22.

Hence the modules 22 are each provided with phase- corrected data representing top, alignment and curvature of the track 11. The module 22 provides output indicating how the corresponding vehicle will react on such a track. This data may be supplied via the post- processing server 14 and the reporting server 16 to a display interface 18. Alternatively it may also be displayed directly on a display unit 24 associated with that module 22. If the module 22 indicates that the corresponding vehicle would give an uncomfortable ride, or would be derailed, or would give unacceptable jolts to the track, this information can also be displayed and a warning given in substantially real-time to an operator.

It will be appreciated that 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.

Although the method has been described as being performed within a track recording vehicle 10, and so giving information in real-time, it will also be appreciated that data previously obtained using a track recording vehicle 10 may be supplied via such a phase correction microprocessor (equivalent to the post processing server 14) to a plurality of vehicle dynamics modules 22 off line.