BL.Optim: A cutting-edge but static planning optimization tool BL.Optim is our optimization engine for routing and scheduling, used to generate efficient planning under complex constraints. While highly flexible, it currently operates as a static solver: schedules are generated once, based on a submitted problem, and cannot be adapted without full recomputation. When plans need to […]
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
Automate the discovery of anomalous behaviour with unsupervised anomaly detection Read More »
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
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
Automatic Detection of Anomalies in systems behaviors Read More »
