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    A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

    Source: Journal of Mechanical Design:;2023:;volume( 146 ):;issue: 002::page 20902-1
    Author:
    Karkaria, Vispi
    ,
    Chen, Jie
    ,
    Siuta, Chase
    ,
    Lim, Damien
    ,
    Radulescu, Robert
    ,
    Chen, Wei
    DOI: 10.1115/1.4063761
    Publisher: 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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      A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

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    contributor authorKarkaria, Vispi
    contributor authorChen, Jie
    contributor authorSiuta, Chase
    contributor authorLim, Damien
    contributor authorRadulescu, Robert
    contributor authorChen, Wei
    date accessioned2024-04-24T22:40:20Z
    date available2024-04-24T22:40:20Z
    date copyright11/13/2023 12:00:00 AM
    date issued2023
    identifier issn1050-0472
    identifier othermd_146_2_020902.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295655
    description abstractIn 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires
    typeJournal Paper
    journal volume146
    journal issue2
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4063761
    journal fristpage20902-1
    journal lastpage20902-11
    page11
    treeJournal of Mechanical Design:;2023:;volume( 146 ):;issue: 002
    contenttypeFulltext
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