Abstract Connected vehicle analytics has a promise to substantially advance vehicle prognostics and health management. However, the practical implementation of connected vehicle prognostics faces a number of challenges, such as the limitation of communication bandwidth resulting in potential loss of data that is critical for adequate prognostics models. The paper discusses a modelling framework for connected vehicle prognostics for dynamic systems that allows addressing connectivity limitations and memory constraints. The framework is based on a hybrid prognostics approach combining in-vehicle physics-based data aggregation model and cloud-based data-driven prognostics leveraging cross-vehicle and external data sources. The application of the framework is illustrated by models for brake pads wear and cabin air filter prognostics.


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    Title :

    Connected Vehicle Prognostics Framework for Dynamic Systems


    Contributors:


    Publication date :

    2018-12-07


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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