Abstract
We use Graph Neural Networks on signature-augmented graphs derived from time series for Predictive Maintenance. With this technique, we propose a solution to the Intelligent Data Analysis Industrial Challenge 2024 on the newly released SCANIA Component X dataset. We describe an Exploratory Data Analysis and preprocessing of the dataset, proposing improvements for its description in the SCANIA paper.
C. Metta—EU Horizon 2020: G.A. 871042 SoBig-Data++, NextGenEU - PNRR-PEAI (M4C2, investment 1.3) FAIR and “SoBigData.it”.
M. Parton—Funded by GNSAGA INdAM group.
M. Parton, A. Fois, M. Vegliò, C. Metta, M. Gregnanin—Computational resources provided by CLAI laboratory, Chieti-Pescara, Italy.
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Parton, M., Fois, A., Vegliò, M., Metta, C., Gregnanin, M. (2024). Predicting the Failure of Component X in the Scania Dataset with Graph Neural Networks. 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_20
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