Annotating The Performance Of Industrial Assets Via Relevancy Estimation Of Event Logs

Nowadays, more and more industrial assets are continuously monitored and generate vast amount of events and sensor data. It provides an excellent opportunity for understanding the asset behaviour that is currently underexplored due to several challenges: extremely heterogeneous data sources, overwhelming data volume, textual aspect of event logs and complex relational dependencies between events. We have addressed this problem by developing two methodologies: 1) A methodology to detect the relevant events while taking into account the relations between them 2) A methodology (built on top of the first one) to build performance profiles taking into account multiple data sources (events and sensor data). We have validated the methodologies in the specific photovoltaic (PV) domain.