The PHM data challenge 2021 is here, and the PrimaVera consortium has jumped on board! A team consisting of PhD/PostDoc researchers Núbia Alves da Silveira, Thom Badings, Luc Keizers, Lisandro Jimenez and Matthias Volk is working to address the challenges of fault detection, classification and root cause identification using data-driven, physics-based and hybrid state-of-the-art algorithms. More about this coming soon!
No more train delays, power outages, or failure of production machines? The PrimaVera project, funded by the Dutch National Research Agenda (NWA), represents a major step towards this goal. With predictive maintenance, or just-in-time maintenance (maintenance just before a system breaks down), the reliability of infrastructure and production resources can be increased and the costs of maintenance can be reduced.
Existing predictive maintenance techniques only work for small-scale systems and are difficult to scale up. Choices made in one place in the chain have an important influence on other processes in the chain. The choice of a certain type of sensors and measurements influences the type of predictions that can be made, and therefore also the quality of the predictions. That is why cross-level optimization methods are being developed within PrimaVera.