Abstract
In industrial context, event logging is a widely accepted concept supported by most applications, services, network devices, and other IT systems. Event logs usually provide important information about security incidents, system faults or performance issues. In this way, the analysis of data from event logs is essential to extract key information in order to highlight features and patterns to understand and identify reasons of failures or faults. Our objective is to help anticipate equipment failures to allow for advance scheduling of corrective maintenance. We propose a supervised approach to predict faults from an event log dataset using wavelets features as input of a random forest which is an ensemble learning method.
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Acknowledgements
We would like to acknowledge ENEDIS for this collaboration and we especially thank Pierre Achaichia, Paul Mersy and Thomas Pilaud from ENEDIS for rich discussions.
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Bonnevay, S., Cugliari, J., Granger, V. (2020). Predictive Maintenance from Event Logs Using Wavelet-Based Features: An Industrial Application. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_13
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DOI: https://doi.org/10.1007/978-3-030-20055-8_13
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