Method for predicting wheel profile parameters
The implementation of TOF sensors for continuous wheel profile monitoring in rail vehicles addresses the inefficiencies of manual inspections by enabling real-time, cost-effective, and reliable prediction and management of wheel wear, thereby enhancing maintenance efficiency and safety.
Patent Information
- Application Number
- DE102024201492
- Authority / Receiving Office
- DE · DE
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2024-02-19
- Publication Date
- 2025-08-21
AI Technical Summary
Existing methods for determining wheel profile parameters in rail vehicles are time-consuming, costly, error-prone, and difficult to implement due to the need for regular manual inspections in workshops, which are inefficient and costly.
Utilizing Time-of-Flight (TOF) sensors, preferably ultrasonic-based, to continuously monitor wheel profile parameters during operation, comparing them with reference values, and using a control system and database to predict future changes and initiate timely interventions.
Enables continuous, real-time monitoring of wheel profiles, reducing maintenance costs and personnel requirements while ensuring timely interventions and preventing damage to railway infrastructure by predicting and managing wheel wear.
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Abstract
Description
[0001] The invention relates to a method for predicting wheel profile parameters in a rail vehicle.
[0002] The wheel profile of rail vehicles, i.e. the part of the wheel that ensures stable contact between the wheel and the rail, is manufactured according to a standardized geometry, for example in accordance with the standard DIN EN 13715.
[0003] Due to dynamic metal (wheel)-metal (rail) contact, the wheel profile is subject to wear. This wear initially manifests itself in the form of a geometric change in the wheel profile, which deviates from the profile originally required by the standard or from the "as new" profile.
[0004] Fig. Figure 3 shows two wheel profiles in cross-section in a well-known Cartesian diagram representation, which illustrates a possible type of profile wear.
[0005] In the diagram, a first, new wheel profile is shown with the term “New”, while in comparison, possible signs of wear on the wheel profile are shown with the term “Worn”.
[0006] The horizontal axis of the diagram shows a width of the wheel profile, called “width,” in different areas, namely a first area called “flange” and a second area called “tread.”
[0007] Accordingly, the vertical axis of the diagram shows a flange height referred to as “height” in relation to the areas of the horizontal axis.
[0008] It is known that corrective turning processes, in which wheel material is removed, can restore a required wheel profile or wheel profile parameters to a wheel. This wheel profile correction is referred to as "reprofiling."
[0009] To determine the need for reprofiling, the wheel profile parameters are determined by regular measurements on the wheel, by recording the wheel diameter and profile using special instruments. The corresponding specifications for wheel profile parameters are set out in Fig. 3 shown with the labels: Sd for the flange thickness, Sh for the wheel flange height, TH for the hollow barrel (“Tread-Hollow”) and qR is the wheel transverse dimension, which represents a flange angle (see standard DIN EN 15313, section 5.2.2 (Figure 9) including the associated legend).
[0010] The determination of the wheel profile parameters using special instruments requires that the corresponding rail vehicle is located in a suitable workshop with trained and error-free personnel.
[0011] This determination of the safety-critical wheel profile parameters is therefore time-consuming, cost-intensive, error-prone and difficult to carry out or plan due to the desired long or uninterrupted operating times for a rail vehicle.
[0012] It is therefore an object of the present invention to provide an improved method to enable an improved prediction of wheel profile parameters.
[0013] This problem is solved by the features of patent claim 1. Advantageous further developments are specified in the dependent patent claims.
[0014] In the method according to the invention for predicting wheel profile parameters, wheel profile parameters associated with a wheel of a rail vehicle are determined and used as reference values.
[0015] During operation of the rail vehicle, changes in the wheel profile parameters are continuously detected using at least one sensor.
[0016] Based on the reference values and the determined changes in the wheel profile parameters, a temporal prediction is made for future expected wheel profile parameters.
[0017] In a preferred development, a TOF sensor is used as the sensor, which uses a signal-based time-of-flight method to determine a distance between the sensor and a point on the wheel profile assigned to the sensor, wherein the sensor is mounted on the rail vehicle in a fixed position with respect to the wheel profile.
[0018] TOF (time-of-flight, or TOF) sensors use a time-of-flight (TFS) method to measure distances. Using one or more TOF sensors, the respective distances from the sensors to predetermined points on the wheel profile are measured to determine the wheel profile parameters. Changes in the wheel profile or altered wheel profile parameters can thus be determined based on the measured distance changes.
[0019] In a preferred embodiment, a TOF sensor is used which uses sound waves, in particular ultrasonic waves, to measure distance.
[0020] Ultrasonic-based TOF sensors use sound waves to measure distances between the sensor and the wheel surface or wheel profile with high precision. The TOF sensor has a transmitter that emits pulses of high-frequency sound waves, which are above the audible threshold of the human ear, toward the wheel surface. These sound pulses hit the wheel surface and are reflected back to the sensor. The TOF sensor measures the time that elapses between the transmission of the pulse and the reception of the reflected pulse. Since the speed of sound in air is known and constant, the distance between the sensor and the wheel surface can be determined or calculated with high accuracy.
[0021] Compared to light wave-based TOF sensors (laser, infrared), ultrasonic-based TOF sensors have the advantage that contaminants (grease, dust, ...) on the wheel surface are penetrated by the ultrasonic signal, so that these contaminants have no or negligible influence on the distance measurement.
[0022] At the same time, the use of ultrasonic waves offers the advantage of being inaudible and harmless to the human eye, making their use in a rail vehicle safe.
[0023] In a preferred embodiment, a support is used to mount the sensor at a predetermined distance from the wheel profile. The support is preferably attached to a bogie frame of the rail vehicle. The support allows the TOF sensor to be positioned at a distance (in the range of a few centimeters) from the wheel profile that is optimized for measurement.
[0024] In a preferred embodiment, the carrier is shaped such that it approximates the wheel profile of the wheel to be monitored. This allows the carrier to mount multiple sensors at a consistent, optimized distance relative to the wheel profile.
[0025] In a preferred embodiment, the predetermined distance is set when the wheel is reprofiled or when the wheel is replaced.
[0026] In a preferred further development, the wheel profile parameters used as reference values are determined during the reprofiling of the wheel or during the wheel replacement.
[0027] In a preferred development, the wheel profile parameters used as reference values are determined with the aid of at least one sensor.
[0028] In a preferred further development, the wheel profile parameters used as reference values are recorded by a control system of the rail vehicle.
[0029] In a preferred further development, the control system compares the continuously monitored wheel profile parameters with the reference values in order to determine the changes in the wheel profile parameters.
[0030] In a preferred further development, the control system compares the continuously monitored wheel profile parameters with previously known permissible limit values.
[0031] In a preferred further development, the control system stores the wheel profile parameters in a database as the mileage of the rail vehicle increases in order to map a history of the wheel's wear.
[0032] In a preferred further development, in addition to the wheel profile parameters, position data and / or operating data of the rail vehicle are stored in the database as further information that is assigned to the wheel profile parameters in order to depict a history of the wear progression of the wheel that is improved in terms of accuracy.
[0033] In a preferred further development, the database is used to make predictions about expected developments for the (monitored) wheel under previously known operating conditions.
[0034] In a preferred training course, the database is used to ensure compliance with the limit values. If a critical intervention value is determined for a limit value, follow-up measures are initiated (e.g., an inspection is initiated, a warning mechanism is activated, or emergency braking is initiated, etc.). In a preferential training, operators and control personnel receive notification regarding the follow-up measures to be taken.
[0035] In a preferred further development, the control system carries out a plausibility check of the wheel profile parameters.
[0036] For example, during each periodic or continuous detection by the sensor, the control system checks whether the wheel diameter has become smaller due to wear or tear, or whether the wheel flange height or rollover has actually increased due to wear or tear.
[0037] In a preferred further development, the database is used to train a learning algorithm. This learning algorithm is then used to determine predictions regarding expected developments for the wheel under similar or comparable operating conditions.
[0038] The learning algorithm is trained by self-learning to determine a distance between two consecutive inspections.
[0039] The evaluation of the current parameters and the prediction of their future development is carried out by the learning algorithm in an iterative manner.
[0040] For example, a pre-training period is performed in which the algorithm receives input data from TOF sensors, GPS parameters, kinematic parameters, etc.
[0041] At the end of the pre-training period, the algorithm creates an estimate of the assumed current values based on data from the pre-training period.
[0042] The estimate generated by the algorithm is compared with a current recording to determine any discrepancies between the estimate and reality. The discrepancy is then passed to the algorithm for adaptive training, which then takes it into account. Advantages of the invention:
[0043] The present invention enables continuous monitoring of the wheel profile, replacing the periodic inspections previously performed. This means that changes in the wheel profile are detected in real time, enabling timely intervention in the event of critical wear or a dangerous situation (incipient wheel deformation, breakage, etc.).
[0044] The present invention detects in a timely manner any exceedance of permissible operating limits.
[0045] The present invention enables anomalies in the wheel profile to be detected in a timely manner, thus preventing damage to the railway infrastructure, in particular to the rails, caused by damaged wheels.
[0046] The present invention ensures improved resource utilization. Conventional monitoring relies on regularly conducted manual inspections, which require personnel and time. In contrast, the present invention eliminates the need for regular inspections and allows for targeted interventions that are performed only when needed.
[0047] The present invention reduces personnel and maintenance costs due to automated monitoring.
[0048] The present invention enables optimized planning for the maintenance of rail vehicles. The operational interruptions required for maintenance are minimized, and wheel interventions can be planned strategically.
[0049] The present invention achieves increased reliability. Through continuous monitoring and preventive interventions, wheel life is maximized and the reliability of the entire railway system is increased. Character description
[0050] The invention is explained in more detail below with the aid of a drawing. It shows: Fig. 1 in a first representation an arrangement of TOF sensors for wheel profile monitoring in relation to the invention, Fig. 2 with reference to Fig. 1 a second representation of the arrangement of the TOF sensors, and Fig. 3 Wheel profiles in a Cartesian diagram representation according to the state of the art described in the introduction.
[0051] Fig. 1 shows in a first representation an arrangement of TOF sensors for wheel profile monitoring in relation to the invention.
[0052] The illustration shown here is based on the explanations given in the introduction to the figure Fig. 3, and reference is made here to the statements made there.
[0053] To determine the wheel profile parameters, five TOF sensors A, B, C, D and E are used as an example, which are arranged along a distance profile or along a carrier S.
[0054] The support S is preferably made of metal and is preferably attached to a bogie frame of the rail vehicle.
[0055] The carrier S is designed such that it approximates the shape of the wheel profile RP and has a predetermined distance from the wheel profile RP.
[0056] Sensors A to E are used as follows: - Sensors A and D for measuring the parameter Sh, i.e. the wheel flange height, - Sensors A, B and C for measuring the parameter qR, i.e. the wheel cross dimension, - Sensors C and D for measuring the parameter Sd, i.e. the wheel flange thickness, - Sensor D for measuring the wheel diameter and the hollow TH, - Sensor D also serves as a reference point for measuring the parameters Sd, Sh and qR - Sensor E for measuring a profile width, and - Sensor E also serves as a measure for a so-called "overroll," which is not very pronounced in this illustration. Overrolling is shown in the EN 15313 standard under section C.2.11 and is referred to there as S1. In practice, overrolling is detected as a profile width that is a few millimeters larger. For example, if a profile width should be 140 mm, but actually has a width of 143 mm, the overrolling is correspondingly 3 mm.
[0057] Fig. 2 shows with reference to Fig. 1 a second representation of the arrangement of the TOF sensors.
[0058] A support S is provided for each wheel RD of a bogie DG. The support S is rigidly anchored to the bogie frame DGR by means of a mechanical element in order to optimally adjust the distance between the respective sensor and the wheel RD.
[0059] The approach of the carrier S to the wheel profile is adjusted so that a relative movement between wheel RD and the bogie frame DGR does not lead to any interference between wheel RD and the respective sensor.
[0060] If one imagines a clock face above the wheel RD, the ideal positions of the support S are at 3 o'clock and 9 o'clock - i.e. in front of and behind the wheel in the direction of travel and at the level of the wheel axis RA.
[0061] The sensors A to E mounted on the carrier S are electrically connected to a control system of the rail vehicle via wiring using multipolar connectors. QUOTES CONTAINED IN THE DESCRIPTION
[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature
[0000] DIN EN 13715
[0002] DIN EN 15313
[0009]
Claims
[1] Method for predicting wheel profile parameters in a rail vehicle, - in which the wheel profile parameters associated with a wheel of a rail vehicle are determined and used as reference values, - in which changes in the wheel profile parameters are continuously detected during operation of the rail vehicle using at least one sensor, - in which, based on the reference values and the determined changes in the wheel profile parameters, a temporal prediction is made for future expected wheel profile parameters. [2] Method according to claim 1, in which a TOF sensor is used as the sensor which determines a distance between the sensor and a point on the wheel profile assigned to the sensor with the aid of a signal-based time-of-flight method, the sensor being mounted on the rail vehicle in a fixed position with respect to the wheel profile. [3] Method according to claim 2, in which a TOF sensor is used which uses sound waves, in particular ultrasonic waves, for distance measurement. [4] A method according to any one of the preceding claims, wherein a support is used to mount the sensor at a predetermined distance from the wheel profile. [5] Method according to claim 4, wherein the predetermined distance is set during reprofiling of the wheel or during wheel replacement. [6] Method according to one of the preceding claims, in which the wheel profile parameters used as reference values are determined during a reprofiling of the wheel or during a wheel replacement. [7] Method according to claim 6, - in which the wheel profile parameters used as reference values are determined with the aid of at least one sensor, and / or - in which the wheel profile parameters used as reference values are recorded by a control system of the rail vehicle. [8] Method according to claim 7, - in which the control system compares the continuously monitored wheel profile parameters with the reference values to determine the changes in the wheel profile parameters, and / or - in which the control system compares the continuously monitored wheel profile parameters with previously known permissible limit values, and / or - in which the control system stores the wheel profile parameters in a database as the rail vehicle's mileage increases in order to map a history of the wheel's wear. [9] Method according to claim 8, wherein, in addition to the wheel profile parameters, position data and / or operating data of the rail vehicle are stored in the database as further information which are assigned to the wheel profile parameters in order to depict a history of the wear progression of the wheel which is improved in terms of accuracy. [10] Method according to claim 8 or 9, - where the database is used to make predictions about expected developments for the wheel under previously known operating conditions, and / or - where the database is used to ensure compliance with the limit values. [11] Rail vehicle with means designed to carry out the method according to one of claims 1 to 10.
Citation Information
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