Predictive maintenance from event logs using wavelet-based features: an industrial application

Stéphane Bonnevay (1) , Jairo Cugliari (1) , Victoria Granger (2)

1 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances
2 UMR MARBEC - MARine Biodiversity Exploitation and Conservation

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 informations in order to highlight features and patterns to understand and identify reasons of failures or faults. The objective is to help anticipate equipment failures to allow for advance scheduling of corrective maintenance. In this paper, we address the problem of fault detection from event logs in the electrical industry. We propose a supervised approach to predict faults from an event log data using wavelets features as input of a random forest which is an ensemble learning method. This work was carried out in collaboration with ENEDIS, the distribution operator of the electrical system in France.

Domaines: Méthodologie [stat.ME]

Stéphane Bonnevay, Jairo Cugliari, Victoria Granger. Predictive maintenance from event logs using wavelet-based features: an industrial application. 2018. ⟨hal-01856309⟩