A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle TiresSource: Journal of Mechanical Design:;2023:;volume( 146 ):;issue: 002::page 20902-1DOI: 10.1115/1.4063761Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. The proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.
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contributor author | Karkaria, Vispi | |
contributor author | Chen, Jie | |
contributor author | Siuta, Chase | |
contributor author | Lim, Damien | |
contributor author | Radulescu, Robert | |
contributor author | Chen, Wei | |
date accessioned | 2024-04-24T22:40:20Z | |
date available | 2024-04-24T22:40:20Z | |
date copyright | 11/13/2023 12:00:00 AM | |
date issued | 2023 | |
identifier issn | 1050-0472 | |
identifier other | md_146_2_020902.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295655 | |
description abstract | In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. The proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires | |
type | Journal Paper | |
journal volume | 146 | |
journal issue | 2 | |
journal title | Journal of Mechanical Design | |
identifier doi | 10.1115/1.4063761 | |
journal fristpage | 20902-1 | |
journal lastpage | 20902-11 | |
page | 11 | |
tree | Journal of Mechanical Design:;2023:;volume( 146 ):;issue: 002 | |
contenttype | Fulltext |