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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Makke, O., Gusikhin, O. (2019). Connected Vehicle Prognostics Framework for Dynamic Systems. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_1
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DOI: https://doi.org/10.1007/978-3-030-01818-4_1
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