Device and method for determining a speed of a rail-based vehicle

Inventors: Susanne Horn (Tettnang, DE); Axel Mors (Friedrichshafen, DE); Adam Norris (Friedrichshafen, DE); Jens Giesler (Friedrichshafen, DE); David Stock (Friedrichshafen, DE); Tobias Dentler (Meckenbeuren, DE); Christian Carl (Langenargen, DE); Michael Hertkorn (Bodolz, DE)

Assignee: ZF Friedrichshafen AG

B61L25/021 B61L15/0081 B61L25/025 B61L2205/04

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Quick Facts

Patent No.: US 12662175
App. No.: 18/613,684
Granted: Jun 23, 2026
Kind: B2

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Abstract

A device ( 9 ) for determining a speed of a rail-based vehicle with wheels on a predetermined network of routes includes an interface ( 8 ) for collecting one-dimensional or multi-dimensional vibration data ( 5 ) corresponding to vibrations of at least one wheel acting on the rail-based vehicle as an acceleration of the rail-based vehicle. The vibrations are detectable using at least one wireless sensor ( 2 a, 2 b, 2 c, 2 d ) arranged proximate the at least one wheel. A learning module is configured to apply a trained machine-learned model to the vibration data to determine a ground speed. The trained machine-learned model is trained based on a distance traveled and a ground truth speed ( 17 ) and a corresponding portion of the vibration data ( 5 ).

Claims (35)

1 . A device ( 9 ) for determining a speed of a rail-based vehicle with wheels on a predetermined network of routes, comprising:

an interface ( 8 ) for collecting vibration data ( 5 ) corresponding to vibrations of at least one wheel, the vibrations acting on the rail-based vehicle as an acceleration of the rail-based vehicle;

at least one wireless sensor ( 2 a , 2 b , 2 c , 2 d ) arranged proximate the at least one wheel and configured for detecting the vibrations and generating the vibration data ( 5 ) based on the vibrations detected; and

at least one computing unit configured for applying a trained machine-learned model to the vibration data in order to determine the ground speed, wherein the trained machine-learned model is trained based on a distance traveled, a ground truth speed ( 17 ), and a corresponding portion of the vibration data ( 5 ).

2 . The device ( 9 ) of claim 1 , wherein the at least one computing unit is further configured for:

applying an acoustic analysis to the vibration data ( 5 ) in order to generate a raw spectrogram ( 10 );

applying a filter to the raw spectrogram ( 10 ); and

after the filtering, applying normalization ( 15 ) to generate an acceleration spectrogram ( 14 ),

wherein the trained machine-learned model is trained for application to the acceleration spectrogram ( 14 ) in order to determine the ground speed.

3 . The device ( 9 ) of claim 2 , wherein the at least one computing unit is configured to form a short-time Fourier transform (STFT) as an acoustic analysis of one or both of the raw spectrogram ( 10 ) and the acceleration spectrogram ( 14 ).

4 . The device of claim 2 , wherein the at least one computing unit is configured for filtering the raw spectrogram ( 10 ) by applying one or more of a high-pass filter, a low-pass filter, a bandpass filter, and a median filter ( 13 ).

5 . The device ( 9 ) of claim 1 , wherein the at least one computing unit is configured for receiving GPS positions of the rail-based vehicle, and the at least one computing unit is configured to determine the ground truth speed ( 17 ) based on the GPS positions.

6 . The device ( 9 ) of claim 1 , wherein the at least one computing unit is configured to generate the corresponding ground truth speed ( 17 ) when the rail-based vehicle passes through position alarm points.

7 . The device ( 9 ) of claim 1 , wherein the at least one computing unit is configured to continuously retrain the trained machine-learned model based on the determined ground speed and the ground truth speed ( 17 ).

8 . The device ( 9 ) of claim 1 , wherein the at least one computing unit is configured to determine a wheel position and/or rail position based on the network of routes, a required time, and the determined ground speed.

9 . The device of claim 1 , wherein the at least one computing unit is configured for applying one or more of a high-pass filter, a low-pass filter, a bandpass filter, and a median filter ( 13 ).

10 . The device ( 9 ) of claim 1 , wherein the at least one computing device is configured to train the machine-learned model based on the distance traveled, the corresponding ground truth speed ( 17 ), and the corresponding portion of the vibration data ( 5 ), wherein the ground truth speed ( 17 ) is generated based on transmitted GPS positions.

11 . A method for determining a ground speed of a rail-based vehicle with wheels on a predetermined network of routes, comprising:

detecting an acceleration of the rail-based vehicle as one-dimensional or multi-dimensional vibration data ( 5 ) corresponding to vibrations of at least one of the wheels acting on the rail-based vehicle using a wireless sensor ( 2 a , 2 b , 2 c , 2 d ) arranged proximate each of the at least one wheel;

applying a trained machine-learned model to the vibration data ( 5 ) in order to determine a ground speed, wherein the trained machine-learned model is trained based on a distance traveled, a corresponding ground truth speed ( 17 ), and a corresponding portion of the vibration data ( 5 ).

12 . The method of claim 11 , further comprising:

applying an acoustic analysis to the vibration data in order to generate a raw spectrogram ( 10 );

applying a filter to the raw spectrogram ( 10 ); and

after the filtering, applying a normalization ( 15 ) in order to generate an acceleration spectrogram ( 14 ), and

wherein the trained machine-learned model is trained for application to the acceleration spectrogram ( 14 ) in order to determine the ground speed.

13 . The method of claim 11 , wherein the corresponding ground truth speed ( 17 ) is generated when the rail-based vehicle passes through position alarm points.

14 . The method of claim 11 , further comprising determining a wheel position and/or a rail position based on the ground speed, a required time, and the network of routes.

15 . The method of claim 11 , applying one or more of a high-pass filter, a low-pass filter, a bandpass filter, and a median filter ( 13 ).

16 . The method of claim 11 , further comprising retraining the trained machine-learned model based on the determined ground speed and the ground truth speed ( 17 ).

17 . The method of claim 11 , wherein the ground truth speed ( 17 ) is generated based on transmitted GPS positions.

18 . The method of claim 11 , further comprising:

determining at least one current speed-dependent parameter for calculating a wheel diameter of the at least one wheel based on the vibration data; and

determining wear by comparison with corresponding original speed-dependent parameters for an original wheel diameter,

wherein the current speed-dependent parameter and the original speed-dependent parameter are both based on the same or approximately the same ground speed.

19 . The method of claim 18 , wherein one or both of toothing frequencies of a transmission ( 3 ) and wheel frequencies of the at least one wheel are used as a current speed-dependent parameter, wherein the wear of the at least one wheel is determined based on a frequency shift in the acceleration spectrogram ( 14 ).