Simultaneous failures classification in a predictive maintenance case

Antoine Hubermont, Elio Tuci, Nicola De Quattro

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Résumé

In industry 4.0, Machine Learning coupled with sensors monitoring leverages new ways to optimise maintenance strategies. In a predictive maintenance case, failure diagnoses are an excellent way to prevent any breakdowns. Up to now, failure diagnoses are focused on the classification of only one failure among many (multi-label classification), even if multiple failures can occur simultaneously. This study proposes an extension to classify simultaneous failures with the most popular classification methods such as random forests or artificial neural networks. Validated on a public predictive maintenance dataset, our methodology achieved classification with equal or best accuracy compared to multi-label classification.
langue originaleAnglais
titreESANN 2023 proceedings
Sous-titreEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 4-6 October 2023,
Pages537-542
ISBN (Electronique)978-2-87587-088-9.
Les DOIs
Etat de la publicationPublié - 6 oct. 2023

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