WO2018041569A1

WO2018041569A1 - Device and method for determining the state of railways

Info

Publication number: WO2018041569A1
Application number: PCT/EP2017/070203
Authority: WO
Inventors: Marco Nock
Original assignee: Knorr-Bremse Systeme für Schienenfahrzeuge GmbH
Priority date: 2016-09-02
Filing date: 2017-08-09
Publication date: 2018-03-08
Also published as: DE102016116415A1

Abstract

The invention relates to a method for determining the state of railways, wherein by means of an available sensor system of at least one rail vehicle at different times and under different environmental conditions measurement data (20, 21), which have been collected while the at least one rail vehicle was travelling on a railway, are input and wear states (24) of the railway are determined from the measurement data (20, 21). The invention further relates to a device for determining the state of railways.

Description

DESCRIPTION

The invention relates to a method for determining the condition of railways. The invention further relates to a device for determining the state of

Railways.

It is known to measure and check railways using special vehicles. This is done at regular intervals and requires vehicles that are equipped with special sensors, as well as separate journeys with these vehicles via the railways. Therefore, these test drives make due to the required special vehicles, the required personnel and thereby required route occupancy as consuming.

The invention is therefore based on the object to provide an improved method and an improved device for detecting wear conditions of railways. This object is achieved by a method for determining the state of

Railways with the method steps of claim 1 and by a

Device for determining the state of railways solved with the features of claim 10. Further features of the invention will become apparent from the

Dependent claims.

In a method according to the invention for determining the state of

Rail tracks are read data measured by existing sensors of at least one rail vehicle at different times and under

different environmental conditions when driving the at least one

Rail vehicle can be detected on a railway. From the

Measurement data is determined wear conditions of the rail track. The measurement data from which the wear conditions are determined are measurement data which can be detected by means of the sensor technology usually present in a normal rail vehicle. These are the sensors that are required in the vehicle for normal driving. Therefore, no additional sensors are needed, as required for example in special vehicles for track inspection.

The measurements are preferably made during the usual trips of

Rail vehicles carried over the railway track and the data in the

Rail vehicle recorded. This may be the data from a single rail vehicle, but preferably the data collected by multiple rail vehicles. The recording of the measured data, which are read in the method according to the invention, can take place at different times and under different environmental conditions. Under the environmental conditions, for example, temperatures, humidity, air pressure, but also

Configurations of the train such as trailer loads, the number of axles of the measuring

Rail vehicle etc. understood.

The measurements, the measurement data of which are based on the method according to the invention, are detected when driving the rail vehicle or vehicles on a rail track. These are preferably conventional operations and

Mostly not special trains with the rail vehicles, as they would be required in the determination of the condition of railways by means of special vehicles. The measurement data can be recorded at different times, that is, either at intervals of a few hours, days, but also weeks, months or years. This also allows historical data to be taken into account when determining the state of wear of the rail track.

The data recorded with the rail vehicles are stored in the

inventive method preferably by an offline processing unit, i. not in a rail vehicle itself, but preferably in a stationary device, read. This means that the data is first extracted from the

Rail vehicles to the process of the invention exporting unit be transmitted. This can be done in different ways, for example through a wireless network.

The wear conditions of the rail track are then determined from the measured data. As a result, the wear on each individual position of the rail track, and thus over the course of the rail track, determined and output. This can be done, for example, by analyzing the courses of measured data when passing known wear conditions and by determining the patterns of the wearer

Measurement data concrete wear conditions are assigned. The assignment of the wear conditions based on patterns can thus be carried out on the basis of experiments in which specific wear conditions of a route are determined or measured, and these known wear conditions are compared with the existing sensors of a rail vehicle measured course of the measured variables, characteristic of the driving over a track section with the known state of wear is.

In an advantageous embodiment of the method, the read measurement data are measurement data of acceleration sensors. Thus, mainly or exclusively, the measurements of existing in a vehicle will be

Acceleration sensors used to determine wear conditions of the rail track. In the acceleration sensors, in particular the

Acceleration sensors that sense accelerations in the vertical direction, i. detect perpendicular to the rail, used. The acceleration sensors, which measure accelerations in the transverse direction, can also be used. There

Accelerations are usually already measured in all directions, by using SD acceleration sensors, accelerations in the longitudinal, transverse and vertical directions can also be used.

On the basis of the measured accelerations, it is possible, for example, to detect rail fractures, track position errors or roughening of rails. In particular, based on the measured lateral accelerations, for example, dissolved

Rail clamps or screwing or displacements of the rail track are detected. The acceleration sensors can, for example, to the Bogies be arranged. The determination of wear conditions can be achieved, for example, by comparing measured accelerations when driving on new or normal rail tracks with measured accelerations when driving on the section to be measured

be performed.

In a further advantageous embodiment of the method, the read measurement data is data of at least one weighing valve of a brake system. A weighing valve measures the load on the wheelset or bogie. By the data of the at least one weighing valve, the vertical load of the rail track can be determined. If all bogies are equipped with weighing valves, then the load can also be determined in bogies.

In a further advantageous embodiment of the method, the read measurement data of an air conditioner. The current air conditioning capacity of an air conditioner is given given environmental conditions of the number of people in the room to be air conditioned, and thus on the number of passengers in the rail vehicle to be conditioned. Based on the air conditioning performance so that the numbers of passengers can be determined or estimated. Thus, on the one hand, the vertical load of the rail track can be determined, but on the other hand, the effect of loading the rail vehicle on the vehicle's center of gravity, and thus the impact on loads during cornering and acceleration operations, i. in a predominantly horizontal direction. The data can also be provided by further sensors of the air conditioning system, for example a CO2 sensor.

In a further advantageous embodiment of the method, the read measurement data is data of at least one speed sensor. Thus, on the one hand the vehicle deceleration or the vehicle acceleration, and thus the load of the rail track in its longitudinal direction can be determined. Furthermore, the slip can be determined using the data of the at least one speed sensor, and thus the load of the rail track in the wheel-rail contact, ie the punctual wear. In a further advantageous embodiment of the method, the read measurement data are data of the sensor system of at least one bogie. This may be, for example, data of one or more arranged on the bogie

Act acceleration sensors. This damage to the rail track, such as a rail break can be detected. When a rail breaks, the acceleration sensor detects a single acceleration. This migrates, if several bogies are equipped with sensors, through the train. In contrast, a wheel damage would result in recurring accelerations whose

Refresh rate depends on the speed of the rail vehicle.

In a further advantageous embodiment of the method, events are detected that have a special influence on the wear of the rail track, and the

Wear conditions of the rail track are determined taking into account the events that have a particular influence on the wear of the rail track.

This can be used to detect events that occur during normal operation

Rail vehicle are not regularly or with less likelihood, and in particular on the normal roles of a wheel on a rail

go out. These are less frequently occurring and / or

extraordinary events. However, these events can be special

Have an impact on wear, especially high wear

towards a normal operation, so that these events should be taken into special consideration when determining the wear. Although wear phenomena caused by these singular events can in principle also be determined, for example, from measurement data recorded by acceleration sensors. However, the accuracy of determining wear conditions can be increased if such events are taken into account directly in the wear determination. In addition, this allows a comparison between the measurements of, for example, acceleration sensors, and the known for these positions singular events, so that patterns can be determined, showing how a measurement of an acceleration sensor for a position of a rail track, where such a singular event has taken place. As a result, the method for determining wear states from the measured data and the assignment of wear conditions to observed progressions of the measured variables can be improved. In a particularly advantageous embodiment of the method, as events which have a particular influence on the wear of the rail track, one or more of the events slip-on slip or spin of drive wheels,

Sandungsvorgang, Gleitschutzauslösung or use of rail brakes, such as a magnetic rail brake detected.

These events are singular events which, compared with the normal rolling of a wheel on the rail, cause a particularly high degree of wear on the rail track, and in particular on the rail surface. By capturing these events, the database can be used to determine the

Wear condition can be improved. In particular, as stated above, this information about the particular events can be compared with the further measurement data, in particular data from acceleration sensors, in order to increase the accuracy of the determination of wear conditions and

In particular, in order to correlate the further measurement data with these wear events and to be able to recognize patterns from these measurement data which are characteristic of the singular events mentioned, so that it is possible to deduce without knowledge of the wear events from the data of, for example, acceleration sensors Position a slip, a spin of drive wheels or another event has taken place.

In one embodiment of the method, position data are assigned to the measured data after the data have been read in. However, the assignment of position data can also already be done in the measuring vehicle by a measurement data record with, for example, from a GPS system position data obtained, ie provided with a place stamp. Irrespective of whether the assignment of position data already takes place during the measurement in the vehicle or only in the method according to the invention during the evaluation, each measured value can thus be assigned a position, ie a position on the route at which the measurement is taken. In a further embodiment of the method, data of the environment of the rail track are taken into account when determining the wear conditions. In this case, for example, temperatures, pressures or humidity or the specific location, such as the location in a tunnel or in a slope can be considered. In an advantageous embodiment of the method, the current state of wear of the rail track is determined. This makes it possible to assess which wear is present at which position of the rail track. As a result, maintenance recommendations, speed recommendations, etc. can be derived. This allows a

Inventory of the current condition of the railway.

In a particularly advantageous embodiment of the method, the development of the state of wear of the track is predicted from the measured data. Thus, it is possible to predict how the state of wear of the line under certain loads, either continuing the current loads, i.

For example, the previous number of train movements or the tensile loads, or under a modified stress, such as an increase in the tensile density, will develop. In addition to the current measurement data, measurement data from the past can be used, so that a comparison of current and earlier measurement data can be used to determine a development of the wear of the track and from this a prediction of future wear development

can be made.

In a further advantageous refinement of the method, the determination of the wear conditions of the rail path assigned to the position data takes place by means of big data methods, for example artificial neural networks. Big-data methods, also referred to as mass data methods in Germany, describe methods that can be used to evaluate large and / or complex and / or weakly structured and / or rapidly changing amounts of data.

If, for example, artificial neural networks are used as an embodiment of big-data methods, the artificial neural networks are first to be trained on the basis of data in which measurement data that passes when a motor vehicle passes over it Track section are measured with a known state of wear, the known state of wear or this state characterizing variables

(for example, roughness). For this purpose, measurements are to be carried out, for example by means of test vehicles, in which the wear of the rail is measured directly, for example by measuring the roughness depth of the rail.

for example, by optical or ultrasonic measuring methods. This training data thus assign measured wear to the measurement data that the sensor system present in the vehicle outputs when driving over a section of track with such a state of wear. This allows the artificial neural networks to be trained.

When determining the wear conditions of the position data associated with the

Railway track by means of artificial neural networks is then assumed by a trained neural network. The trained artificial neural network then obtains the measured data from the sensors of the vehicle as input data when determining the state of wear from the measured values when using the method and then outputs the state of wear to a respective position of the rail path. Due to the artificial neural networks, it is possible to evaluate the measured data with comparatively low computing power and with a relatively low computing power from the measured data the state of wear of the

Determine rail route.

As an alternative to artificial neural networks, however, other big-data methods can also be used for the evaluation, as described above.

In a further advantageous refinement of the method, prior to determining the wear conditions of the rail path assigned to the position data from the measured data, preprocessing of the data is performed separately for the respective ones

Rail vehicles made. This preprocessing can be done online in the vehicle, but it can also take place offline in particular in a stationary evaluation unit. As a result, certain processing steps can be brought before the actual determination of the state of wear from the measured data.

For example, in preprocessing, the events that have special influence on the wear of the railway, for example, the use of

Rail brakes or sandings, with the measured data from the sensors of the

Vehicle, such as acceleration sensors, are correlated. Also here the measurement data can be assigned the respective position, as far as this is not already done in the vehicle.

A device according to the invention for determining the condition of railways comprises means for reading and storing by means of existing sensors of at least one rail vehicle at different times and under

different environmental conditions when driving the at least one

Rail vehicle recorded on a rail measurement data and means for determining wear conditions of the rail track from the measured data.

The measured data are thus carried out, as in the description of the method carried out by sensors that are standard on a rail vehicle and are required for normal driving. The vehicle is not equipped with special sensors, such as special vehicles, which are specially required for monitoring and recording the condition of railways.

The device according to the invention has means with which these measured data can be read. The measured data can come from one and in particular from several rail vehicles. The measurement data are preferably read offline in a stationary device and processed. If it is in particular data of a single vehicle, the reading and the processing can also be done on board the vehicle, for example in real time. The measurement data are read in the device and stored. The measurement data are data that can be acquired by one or more vehicles, in particular at different times and under different environmental conditions. Thus historical data, that is data from measurements taken at different time intervals, can also be taken into account. The device also has means with which the wear conditions of the rail track can be determined from the measured data. Under wear, for example, the roughness or waviness of the rail surface, for example, when corrugation, or local damage such as rail break understood.

With the device according to the invention, it is possible to change the state of

Determine railways without the need for special vehicles equipped with special sensors and requiring special measuring runs. In one embodiment, the device has means for assigning position data to the measurement data. The position data can be detected in the vehicle by GPS, for example, and can already be assigned to the measurement data in the vehicle. The measurement data are then already provided in the vehicle in addition to a time stamp with a place stamp. The assignment of the position data to the measured data can also be made offline. By assigning the position data, it is possible to give a measurement a specific position along the railroad

assign to determine the location of the respective wear can.

In an advantageous embodiment of the device, the device further comprises means for reading and storing events that have a special influence on the wear of the rail track, as well as means for determining

Wear conditions of the rail track, which are assigned to the position data, taking into account the events that have a special influence on the wear. This makes it possible to carry out special events which occur only rarely during normal driving but which lead to a high degree of wear of the rail track, such as sanding, starting slippage or spinning of drive wheels, for example.

Anti-slip protection or the use of rail brakes, to be considered. As already stated in the method, this makes it possible, on the one hand, to increase the accuracy of the determination of the wear conditions, and on the other hand, to correlate the measurement data, for example measurements of acceleration sensors, with the events, which have a particular influence on the wear, so that patterns are determined can be obtained from the measurements with those existing in the rail vehicle Sensors to the wear by the special singular events close.

In a further advantageous embodiment, the device is designed so that it can determine the wear of the rail travel exclusively or predominantly from data provided by existing in the rail vehicle acceleration sensors. These are preferably

Acceleration sensors that detect vertical accelerations, i. Detect accelerations in the direction of the rail. These acceleration sensors may preferably be arranged on the bogie. The sensors are the sensors that are standard in the vehicle to ensure driving. There is thus no need for sensors that serve no purpose other than wear detection. In further advantageous embodiments, the device is designed so that it can determine the wear of the rail track using data provided by at least one present in the rail vehicle weighing valve, and / or by an existing in the rail vehicle air conditioning and / or existing in the rail vehicle Speed sensor and / or by the sensor system of at least one existing in the rail vehicle bogie.

In a further embodiment of the device, the device has means to predict the development of the state of wear of the track. This makes it possible not only to recognize the current state of wear from the measured data, but also to make predictions about the future development and, based on the current and the predicted state of wear, recommendations for maintenance or driving, for example in the form of

Speed limits, spend. A prognosis of the

Wear state can be done in the device by means that evaluate both current measurement data as well as past measurement data taken over different periods of time in the past. As a result, the device is suitable for making statements about a future change in the wear of the track. This allows using the predicted wear development of the rail Recommendations for maintenance and repairs, for example, for the grinding of rails derived.

Embodiments of the invention will be described below with reference to the accompanying drawings.

Show it:

Fig. 1 shows the procedure for determining wear conditions of a

Railway way according to a first embodiment of the invention; and

Fig. 2 shows the procedure for determining wear conditions of

Railways according to a second embodiment of the invention.

In the method illustrated as a general procedure in FIG. 1, measurement data originating from vehicle sensors are read in in step S10. These data come from sensors that are part of the standard equipment of the vehicle and are required for normal vehicle operation or diagnostics of the vehicle components. This is not data captured by special sensors, such as those used in special vehicles for measuring

Railways are installed. The read measurement data can be, for example, measurement data from acceleration sensors. If, for example, 3D acceleration sensors are installed in the vehicle, data measured by these sensors can be used. But also the data of only one spatial direction measuring acceleration sensors, in particular for measuring the vertical acceleration and / or the lateral acceleration can be used.

The data may also include data from brake control weighing valves, air conditioning data, speed sensor data or data from the

Bogie sensors act.

The data is preferably data from multiple vehicles taken at different times and under different conditions. This also allows data from the past to be used whereby the wear of the railway track in the past can be analyzed and observed up to the present time as well as prognoses can be made. The recording of the measured data can take place at different times and under different environmental conditions, for example at different temperatures, humidities, air pressures, but also at different temperatures

Configurations of the train, for example a variation of towed loads, number of axles of the measuring rail vehicle etc.

The data of the vehicle sensors are transmitted from the vehicle to the device that performs the evaluation. The evaluation steps are preferably performed by an off-line processing unit, i. one not in one

Rail vehicle itself, but preferably in a stationary

Evaluation device, which is located outside the rail vehicle carried out. In the establishment, the evaluation and determination of the

Wear state of the data performs, it is thus preferably a stationary offline device. The data must therefore be transmitted from the vehicles to the facility. This can be done by wireless communication, but also on other transmission paths. For this purpose, the data is temporarily stored, for example, in the vehicle or in other stationary facilities.

For the determination of the state of wear of the railroad track, in the method illustrated in FIG. 1, in addition to the vehicle measurements, too

Wear event information is used. The data for this will be

Usually detected by the vehicles and cached and can be transmitted in various ways of data transmission, for example via radio or via temporary storage in an in-vehicle or in an external memory, to the device which determines the state of wear.

In this case, in the embodiment of the method illustrated in FIG. 1, events are recorded which have a particular influence on the wear of the rail track. These events may include, for example, the occurrence of start-up slip, spin of drive wheels, sanding, anti-skid deployment, or use of rail brakes. These events are those to events that cause a particularly high wear of the rail track, and in particular the rail surface. By capturing these events, the information base for determining the state of wear is improved. This information about the particular events can be compared with the further measurement data, in particular the data measured by means of acceleration sensors, in order to increase the accuracy of the determination of wear conditions and to correlate the further measurement data with these wear events. From these measurement data, for example, patterns can be detected which are characteristic of the singular events mentioned, so that it can be concluded even without knowledge of the wear events from the data of acceleration sensors, for example, whether at a position a slip, a spin of drive wheels or a another event took place.

The measurement data and the wear events are read in in step S12.

The measurement data and the wear events may be optional in step S14

Position data to be assigned. Insofar as position data have already been assigned to the measured data and the wear event information in the vehicle, for example from GPS systems of the vehicle, a subsequent assignment of

Position data not required.

In step S16, out of the measurement data and the wear events

Wear conditions of the rail track, which can be assigned to the respective positions along the rail track determined. As a result, the wear on each individual position of the rail track, and thus over the course of the

Railway track, determined and issued. This can be done, for example, by the fact that courses of measured data when driving over known

Wear conditions are analyzed and assigned to the measured patterns of the measured data specific wear conditions. The assignment of

Wear conditions based on patterns can thus take place on the basis of tests in which concrete wear conditions of a section are determined or measured, and these known wear conditions are compared with a course of the measured variables measured with the existing sensors of a rail vehicle. which is characteristic for driving over a section of track with the known state of wear.

For example, the determination can be made on the basis of artificial neural networks which are trained in such a way that they can be calculated from the measured data and the

Wear Events conclusions about the state of wear of the railway track, for example, information about roughness of the

Rail surface, ribbing or rail breaks are issued. However, other big data methods can be used to solve this problem

Determine wear state of the rail track from the measured values. It may also take into account data from the environment of the railway. Thus, for example, temperatures, pressures or humidity or even the specific location, such as the location in a tunnel or in a slope can be considered.

On the basis of, for example, the measured accelerations, rail fractures, track position errors or roughening of rails can be detected, for example. In particular, based on the measured lateral accelerations, for example, dissolved rail brackets or screw connections or displacements of the

Railways are recognized. If acceleration sensors are used for the measurements, they can be arranged, for example, on the bogies. The determination of wear conditions can be carried out, for example, by comparing measured accelerations when driving on new or normal rail tracks with measured accelerations when driving on the section to be measured.

Before determining S16 of the wear conditions of the rail track from the measured data, a preprocessing of the data can be carried out separately for the respective rail vehicles. This preprocessing can also already by a in the

Railway vehicle arranged evaluation unit are made. As a result, the data volumes to be transmitted from the rail vehicle to a stationary evaluation unit can be reduced. The wear state determined in step S16 can be processed and output, for example in graphical representation. Maintenance recommendations or recommendations for driving can be issued. Furthermore, as far as wear events and data from vehicle sensors, which originate from past periods, can be used in the determination of the wear conditions, a representation of the development of the state of wear for the

Can be made past, and it can be predicted how the state of wear will develop in the future. FIG. 2 shows a method sequence in which the preprocessing of the vehicle data is first carried out.

To determine the state of wear 24 of the rail track data 20 of the acceleration sensor of the vehicle are used. These are in particular acceleration sensors that accelerate to individual

Components, preferably in the vertical direction to the rail, measure. These may be, for example, acceleration sensors, which are arranged on bogies of the rail vehicle. Furthermore, special wear events 21, such as the use of the rail brake, sanding or start-up slip, are recorded in the vehicle. These data 20, 21 are assigned, for example in the vehicle from a GPS system position data, so that the measured values and the

Wear events positions on the rail can be clearly assigned. The data 20, 21 are transmitted from the rail vehicles to the device, which determines the state of wear from the data. As already stated above, this can be done by wireless communication, or by a separate one

Data acquisition device, which caches the data, and from which the data can be retrieved by the device, which determines the state of wear as needed.

In the method sequence shown in FIG. 2, a preprocessing 22 of the data originating from the individual vehicles is undertaken. It can For example, special features of the individual vehicles are taken into account, for example, sensor types or sensor positions. In preprocessing, data can be filtered, the amount of data can be reduced, and position data can be assigned to the data, if not already done during the measurement in the vehicle.

In the actual evaluation 23, the measurement data 20 of the

Acceleration sensors and the wear events 21 of the various

Vehicles analyzed to determine from them the wear state 24 of the rail track. Furthermore, a graphical representation of the wear state 24 can take place. The wear state 24 can be evaluated. From the evaluation recommendations for maintenance and for driving on the railway track can be derived, for example, for the establishment of slow driving or for load reduction. Furthermore, a prognosis for the course of further development of the

Wear 24 done. For this purpose, historical data 25, in which measurements but also wear conditions at earlier times are stored, can be used. Furthermore, further data sources 26 can be used for the analysis, the evaluation and the prognosis, in which, for example, patterns are stored in which concrete wear conditions are associated with those for them

Wear conditions are correlated by the acceleration sensor 20 of the vehicle measured progressions of the measured values.

LIST OF REFERENCE NUMBERS

S10 Reading measurement data

S12 Reading wear events S14 Assigning position data

S16 Determination of wear conditions

20 data of the acceleration sensor

21 Wear events 22 Preprocessing vehicle data

23 data evaluation

24 wear condition

25 historical dates

26 more data sources

Claims

1 . Method for determining the condition of railways,

characterized by the following steps:

Reading in (S10) by means of existing sensors at least one

Rail vehicle at different times and under different

Environmental conditions when driving the at least one rail vehicle recorded on a rail measurement data (20),

Determining (S16, 23) wear conditions (24) of the rail path from the measured data (20).

2. The method according to claim 1,

wherein the read measurement data (20) are measured data from acceleration sensors.

3. The method according to claim 1 or 2,

wherein the read measurement data (20) data of at least one weighing valve of a brake system.

4. The method according to any one of the preceding claims,

wherein the read measurement data (20) data of an air conditioner.

5. The method according to any one of the preceding claims,

wherein the read measurement data (20) data of at least one

Speed sensors are.

6. The method according to any one of the preceding claims,

wherein the read measurement data (20) are data of the sensor system of at least one bogie.

7. Method according to one of the preceding claims,

whereby events (21) are recorded which have a special influence on the wear of the railway track, and wherein the wear conditions (24) of the rail track are determined taking into account the events (21).

8. The method according to claim 7,

wherein the events (21) one or more of the events start slip,

Include spinning of drive wheels, sanding, anti-skid tripping or use of rail brakes.

9. The method according to any one of the preceding claims,

wherein the measured data (20) and the events (21) position data are assigned (S14) and the determined wear conditions (24) are assigned to the position data.

10. The method according to any one of the preceding claims,

wherein when determining (S16, 23) the wear conditions (24) data of the environment of the rail track are taken into account.

1 1. Method according to one of the preceding claims,

whereby the development of the state of wear (24) of the route is predicted.

12. The method according to any one of the preceding claims,

wherein the determination (S16, 23) of the wear conditions (24) of the railroad track takes place by means of big data methods, for example artificial neural networks.

13. The method according to any one of the preceding claims,

wherein prior to determining (S16, 23) of the wear conditions (24) of the rail track from the measured data (20) a preprocessing (22) of the data is carried out separately for the respective rail vehicles.

14. Device for determining the condition of railways,

characterized in that the device comprises:

Means for reading and storing by means of existing sensors at least one rail vehicle at different times and under different Environmental conditions when driving the at least one rail vehicle recorded on a rail measurement data (20),

Means for determining wear conditions (24) of the rail path from the measurement data (20).

15. Device according to claim 14,

wherein the apparatus further comprises means for assigning position data to the measurement data (20).

16. Device according to claim 14 or 15,

the apparatus further comprising means for reading and storing

Events (21) that have a special influence on the wear of the rail track, as well as means for determining the position data assigned

Has wear states (24) of the rail path in consideration of the events (21).

17. Device according to one of claims 14 to 16,

the device being designed so that it can detect the wear of the rail track from data (20) present in the rail vehicle

Acceleration sensors are provided.

18. Device according to one of claims 14 to 17,

wherein the device is adapted to detect wear of the rail track from data (20) provided by at least one brake control weighing valve provided in the rail vehicle.

19. Device according to one of claims 14 to 18,

the device being designed so that it can determine the wear of the rail track from data (20) generated by a rail vehicle

Air conditioning will be provided.

20. Device according to one of claims 14 to 19,

the apparatus being adapted to detect wear of the rail track from data (20) provided by at least one speed sensor provided in the rail vehicle.

21. Device according to one of claims 14 to 20,

wherein the device is designed so that it can determine the wear of the rail track from data (20) by the sensors of at least one in the

Rail vehicle existing bogie be provided.

22. Device according to one of claims 14 to 21,

the apparatus comprising means for predicting the evolution of the wear state (24) of the railroad track.