Abstract
Predictive maintenance is a crucial yet challenging task in many industrial applications. This work explores a large repository of existing techniques and approaches to process historical data and predict if an asset is at risk of failure. In particular, the operational condition and specification of Scania trucks in heavy-duty applications is considered as part of the IDA 2024 Industrial Challenge.
L. Carpentier and A. De Temmerman contributed equally to the paper.
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Acknowledgement
This research is supported by Flanders Innovation & Entrepreneurship (VLAIO) through the AI-ICON project CONSCIOUS (HBC.2020.2795), the Flemish government under the Flanders AI Research Program, and by Internal Funds KU Leuven (STG/21/057).
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Carpentier, L., De Temmerman, A., Verbeke, M. (2024). Towards Contextual, Cost-Efficient Predictive Maintenance in Heavy-Duty Trucks. In: Miliou, I., Piatkowski, N., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642. Springer, Cham. https://doi.org/10.1007/978-3-031-58553-1_21
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DOI: https://doi.org/10.1007/978-3-031-58553-1_21
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