Failure anticipation is one of the key industrial research objectives with the advent of Industry 4.0. This paper presents an approach to predict high importance errors using log data emitted by machine tools. It uses the concept of bag to summarize events provided by remote machines, available within log files. The idea of bag is inspired by the Multiple Instance Learning paradigm. However, our proposal follows a different strategy to label bags, that we wanted as simple as possible. Three main setting parameters are defined to build the training set allowing the model to fine-tune the trade-off between early warning, historic informativeness and forecast accuracy. The effectiveness of the approach is demonstrated using a real industrial application where critical errors can be predicted up to seven days in advance thanks to a classification model.< Réduire

Mots clés en anglais

Predictive Maintenance (PdM)

Machine Learning (ML

Classification Log data

Data preparation

URI

https://oskar-bordeaux.fr/handle/20.500.12278/110174

DOI

http://dx.doi.org/10.1007/978-3-030-72651-5_22

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Log Data Preparation for Predicting Critical Errors Occurrences

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Log Data Preparation for Predicting Critical Errors Occurrences

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Résumé en anglais

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URI

DOI

Unités de recherche