Intelligent tire wear life estimation method and device based on BP neural network

A technology of BP neural network and wear life, applied in the field of neural network, can solve problems such as difficulties for novices and low accuracy, and achieve the effect of simple operation, overcoming accurate estimation, and reducing time cost and capital cost

Pending Publication Date: 2021-08-10
JIANGSU UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0003] At present, the main detection method for the wear degree of automobile tires is manual detection, which mainly defines and measures the wear degree of the tread pattern by detecting the tread depth of the tire and the tread wear of the tire sho

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  • Intelligent tire wear life estimation method and device based on BP neural network
  • Intelligent tire wear life estimation method and device based on BP neural network
  • Intelligent tire wear life estimation method and device based on BP neural network

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[0065] In order to more clearly illustrate the technical solutions of the present invention, the specific embodiments of the present invention will be described below with reference to the drawings. It will be apparent that the drawings in the following description are merely some embodiments of the invention, and those of ordinary skill in the art will also obtain other drawings according to these figures without paying creative labor. Other embodiments.

[0066] First embodiment of the invention, such as figure 1 As shown, a smart tire wear life estimation method based on a BP neural network, including:

[0067] S10 acquires data sets containing tire pressure, speed, load, and tire riped 2 to 6 steps, and randomly split data sets as training sets, verification sets and test sets;

[0068] S20 creates a BP neural network model, the input of the network model is data in the data set, and the output is tire grinding.

[0069] S30 based on training set, verification set and pre-set ...

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Abstract

The invention provides an intelligent tire wear life estimation method and device based on a BP neural network. The method comprises the steps: obtaining a data set containing tire pressure, vehicle speed, load and tire radial 2-6 order rising modal frequency, and randomly dividing the data set into a training set, a verification set and a test set; establishing a BP neural network model, wherein the input of the network model is data in the data set, and the output of the network model is tire abrasion loss; training a BP neural network model based on the training set and a preset mean square error, determining the structure and network parameters of the BP neural network model, and performing verification by using a verification set; and inputting the data in the test set into the trained BP neural network model, and estimating the wear life of the tire to which the data belongs. Based on the BP neural network technology, a low-cost and high-efficiency prediction method is provided for automobile tire wear life prediction, and the problem of tire life prediction is solved.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a BP neural network-based intelligent tire wear life estimation method and device. Background technique [0002] With the continuous development of the automobile industry, automobiles have gradually become the main means of transportation for people to go out, and the safe driving of vehicles has gradually become the focus of people's attention. At present, more than half of the traffic accidents on expressways in our country are caused by tire wear, most of which are caused by tire blowouts, and the main causes of tire blowouts are severe tire surface wear and abnormal tire pressure. caused by such circumstances. As one of the main components of a car, tires affect the performance and safety of the vehicle during driving. The importance of vehicle tire detection is self-evident, and it can greatly improve the safety of vehicle driving. [0003] At present, the main de...

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Application Information

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IPC IPC(8): G06F30/23G06F30/27G06N3/04G06N3/08G06F111/10G06F119/04
CPCG06F30/23G06F30/27G06N3/084G06F2111/10G06F2119/04G06N3/045
Inventor 全振强李波贝绍轶张兰春赵又群韩霄茅海剑顾甜莉魏书萌杭陶阳
Owner JIANGSU UNIVERSITY OF TECHNOLOGY
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