Predicting tire imbalance and/or wheel misalignment

Patent metadata

Field	Value
Publication number	US-11879810-B2
Application number	US-202017021358-A
Country	US
Kind code	B2
Filing date	Sep 15, 2020
Priority date	Sep 15, 2020
Publication date	Jan 23, 2024
Grant date	Jan 23, 2024

Abstract

An exemplary method includes vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations; a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds. An exemplary apparatus includes a vehicle; tires mounted to the vehicle; a speed sensor mounted to the vehicle; a vibration sensor mounted to the vehicle; and a processor connected in communication with the speed sensor and the vibration sensor. The processor is adapted to implement any of the method steps above.

First claim

What is claimed is:

1. A method comprising:
vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations;
a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and
the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds.

2. The method of claim 1 , further comprising:
the processor scheduling wheel or tire service for the vehicle driver.

3. The method of claim 1 , further comprising:
the processor logging vehicle tire and steering vibration as functions of vehicle speed;
the processor determining, from the logged functions, baseline values of vibration frequency and amplitude corresponding to each of a plurality of vehicle speeds; and
the processor determining, from the baseline values and the logged vibrations, upper bounds corresponding to each of the plurality of vehicle speeds.

4. The method of claim 3 , further comprising: the processor logging steering vibration from a smartwatch worn by the vehicle driver.

5. The method of claim 3 , further comprising: the processor logging vehicle tire vibration from a wheel-mounted vibration sensor.

6. The method of claim 3 , further comprising: the processor logging vehicle speed based on images from a wheel-well mounted camera.

7. The method of claim 3 , further comprising: the processor logging vehicle speed based on data from an accelerometer within the vehicle.

8. An apparatus comprising:
a vehicle;
tires mounted to the vehicle;
a speed sensor mounted to the vehicle;
a vibration sensor mounted to the vehicle body; and
a processor connected in communication with the speed sensor and the vibration sensor, wherein the processor is adapted to:
continuously detect vehicle speed and vehicle tire and steering vibrations by monitoring signals from the speed sensor and the vibration sensor;
continuously compare detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and
alert a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds.

9. The apparatus of claim 8 , wherein the processor is further adapted to:
schedule wheel or tire service for the vehicle driver.

10. The apparatus of claim 8 , wherein the processor is further adapted to:
log vehicle tire and steering vibration as functions of vehicle speed;
determine, from the logged functions, baseline values of vibration frequency and amplitude corresponding to each of a plurality of vehicle speeds; and
determine, from the baseline values and the logged vibrations, upper bounds corresponding to each of the plurality of vehicle speeds.

11. The apparatus of claim 8 , wherein the vibration sensor comprises a smartwatch worn by the vehicle driver and measures steering vibration.

12. The apparatus of claim 8 , wherein the vibration sensor comprises a wheel-mounted vibration sensor and measures tire vibration.

13. The apparatus of claim 8 , wherein the speed sensor comprises a wheel-well mounted camera.

14. The apparatus of claim 13 , wherein the speed sensor comprises an accelerometer within the vehicle.

15. A computer readable storage medium embodying computer executable instructions that when executed by a computer cause the computer to facilitate the method of:
continuously detecting vehicle speed and vehicle tire and steering vibrations;
continuously comparing detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and
alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds.

16. The medium of claim 15 , the method further comprising:
scheduling wheel or tire service for the vehicle driver.

17. The medium of claim 15 , the method further comprising:
logging vehicle tire and steering vibration as functions of vehicle speed;
determining, from the logged functions, baseline values of vibration frequency and amplitude corresponding to each of a plurality of vehicle speeds; and
determining, from the baseline values and the logged vibrations, upper bounds corresponding to each of the plurality of vehicle speeds.

18. The medium of claim 17 , the method further comprising: logging steering vibration from a smartwatch worn by the vehicle driver.

19. The medium of claim 17 , the method further comprising: logging vehicle tire vibration from a wheel-mounted vibration sensor.

20. The medium of claim 17 , the method further comprising: logging vehicle speed based on images from a wheel-well mounted camera.

Assignees

• IBM

Inventors

• Cintas Celia
• Gordon Michael S
• Weldemariam Komminist

Classifications

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06Q10/1093 Primary

  Calendar-based scheduling for persons or groups · CPC title