Indirect tire wear state estimation system

Patent metadata

Field	Value
Publication number	US-2017113495-A1
Application number	US-201514919029-A
Country	US
Kind code	A1
Filing date	Oct 21, 2015
Priority date	Oct 21, 2015
Publication date	Apr 27, 2017
Grant date	—

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.

First claim

What is claimed is:

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.

Assignees

• Goodyear Tire & Rubber

Inventors

• Singh Kanwar Bharat

Classifications

• B60C2019/004

  Tyre sensors other than for detecting tyre pressure · CPC title

• G01M17/02

  Tyres · CPC title

• B60C11/246 Primary

  Tread wear monitoring systems · CPC title

• B60C23/02

  Signalling devices actuated by tyre pressure {(hand-held tyre pressure gauges G01L17/00)} · CPC title

• B60C23/20

  Devices for measuring or signalling tyre temperature {only} · CPC title