INDIRECT TIRE WEAR STATE ESTIMATION SYSTEM

• US 20170113495A1
• Filed: 10/21/2015
• Published: 04/27/2017
• Est. Priority Date: 10/21/2015
• Status: Active Grant

• Current Assignee: Goodyear Tire & Rubber Company
• Original Assignee: Goodyear Tire & Rubber Company
• Inventors: Singh, Kanwar Bharat
• Granted Patent: US 9,821,611 B2
• Time in Patent Office: Days
• CPC Class Codes: B60C 11/246 Tread wear monitoring systems
• CPC Class Codes: B60C 2019/004 Tyre sensors other than for detecting tyre pressure
• CPC Class Codes: B60C 23/02 Signalling devices actuated by tyre pressure hand-held tyre pressure gauges G01L17/00
• CPC Class Codes: B60C 23/0408 transmitting the signals by non-mechanical means from the wheel or tyre to a vehicle body mounted receiver
• CPC Class Codes: B60C 23/20 Devices for measuring or signalling tyre temperature only
• CPC Class Codes: G01M 17/02 Tyres

Abstract

A tire wear state estimation system includes a vehicle-based sensor for measuring wheel speed of a tire-supporting and first and second features extracted from the wheel speed signal. A data classifier conducts a classification of the first extraction feature data relative to the second extraction feature data by a statistical analysis of the wheel speed signal to estimate the wear state of the tire.

Claims

1. A tire wear state estimation system comprising:

a tire supported by a wheel and supporting a vehicle;

a vehicle-based sensor for measuring wheel speed of the wheel supporting the tire and generating a wheel speed signal;

a feature-extracting processor for extracting from the wheel speed signal a first extracted feature;

a feature-extracting processor for extracting from the wheel speed signal a second extracted feature;

a data classifier receiving as data inputs first extracted feature data and second extracted feature data;

the data classifier operable to conduct a classification of the first extraction feature data relative to the second extraction feature data to estimate a wear state for the tire.

2. The tire wear state estimation system of claim 1, wherein further comprising:

a tire-mounted temperature sensor for measuring temperature of the tire;

a tire-mounted pressure sensor for measuring air pressure within the tire;

a tire identification module mounted to the tire for identifying the tire;

an accessible tire construction database operable to identify tire construction characteristics based upon tire identification by the tire identification module; and

wherein the data classifier is operable to adaptively modify the classification of the first extraction feature data relative to the second extraction feature data based upon the measured temperature of the tire, the measured air pressure within the tire and the identified tire construction characteristics.

3. The tire wear state estimation system of claim 1, wherein the first extracted feature is a median slip-ratio of the tire.

4. The tire wear state estimation system of claim 3, wherein the second extracted feature is a slip-ratio rate of the tire.

5. The tire wear state estimation system of claim 4, wherein the median slip-ratio of the tire and the slip-ratio rate of the tire are operably determined by a statistical analysis of the wheel speed signal.

6. The tire wear state estimation system of claim 5, wherein the estimation of the wear state of the tire is operably determined from a support vector data classification algorithm using as inputs the median slip-ratio data of the tire and the slip-ratio data of the tire.

7. The tire wear state estimation system of claim 6, wherein further comprising:

a tire-mounted temperature sensor for measuring temperature of the tire;

a tire-mounted pressure sensor for measuring air pressure within the tire;

a tire identification module mounted to the tire for identifying the tire;

an accessible tire construction database operable to identify tire construction characteristics based upon tire identification by the tire identification module; and

wherein the data classifier is operable to adaptively modify the classification of the first extraction feature relative to the second extraction feature based upon the measured temperature of the tire;

the measured air pressure within the tire; and

the identified tire construction characteristics.

8. A tire wear state estimation system comprising:

a tire supported by a wheel and supporting a vehicle;

a vehicle-based sensor for measuring wheel speed of the wheel supporting the tire and generating a wheel speed signal;

a feature-extracting processor for extracting from the wheel speed signal a first extracted feature, the first extracted feature quantitatively changing responsive to a wear level change of the tire;

a feature-extracting processor for extracting from the wheel speed signal a second extracted feature, the second extracted feature quantitatively changing responsive to a wear level change of the tire;

a data classifier receiving as inputs first extracted feature data and second extracted feature data;

the data classifier operable to conduct a classification of the first extraction feature data relative to the second extraction feature data;

a wear state estimator operable to estimate a wear state for the tire based upon the classification of the first extraction feature data relative to the second extraction feature data.

9. The tire wear state estimation system of claim 8, wherein the first extracted feature is a median slip-ratio of the tire.

10. The tire wear state estimation system of claim 9, wherein the second extracted feature is a slip-ratio rate of the tire.

11. The tire wear state estimation system of claim 10, wherein the median slip-ratio of the tire and the slip-ratio rate of the tire are operably determined by a statistical analysis of the wheel speed signal.

12. The tire wear state estimation system of claim 11, wherein the estimation of the wear state of the tire is operably determined from a support vector data classification algorithm using as inputs the median slip-ratio data of the tire and the slip-ratio data of the tire.