Kevin Ducharlet

Automate the discovery of anomalous behaviour with unsupervised anomaly detection

In details, Research /

Unsupervised anomaly detection makes it possible to automate tasks relating to the maintenance of equipment and infrastructure, which until now have been carried out by experts. One of the features of the BL.Predict equipment management platform is the ability to manually define alert thresholds so that you are warned when the systems being monitored break […]

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Incremental-properties

How to apply Christoffel-Darboux kernel on online anomaly detection with few parameterization

In details, Research /

Incremental properties Kevin Ducharlet is Ph.D. Candidate in the DRIT team. Since a year and a half, he started his thesis entitled: “Certification and confidence in sensor data: detection of outliers and abnormal values in time series.” Sensor data are generated using devices which measure a physical asset’s behaviour. These informations can be used to

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Automatic Detection of Anomalies in systems behaviors

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Nowadays, industrial systems maintenance uses the principle of Digital Twin to predict future issues. Sensors placed on the different parts of the system emit a signal that characterize its behavior. The study of these time signals in real time allow us to detect unusual behaviors preceding a breakdown. However, the sensors signals can be unreliable

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