by Thom Badings

PrimaVera Project

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.

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Nubia Nale Alves Silveira; Richard Loendersloot; Annemieke Angelique Meghoe; Tiedo Tinga: Data Selection Criteria for the Application of Predictive Maintenance to Centrifugal Pumps. In: Proceedings of the 6th European Conference of the Prognostics and Health Management Societ, pp. 372–380, 2021, (6th European Conference of the Prognostics and Health Management Society, PHME 2021, PHME 2021 ; Conference date: 28-06-2021 Through 02-07-2021). (Type: Proceedings Article | Abstract | Links | BibTeX)
Luc S. Keizers; Richard Loendersloot; Tiedo Tinga: Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions. In: International Journal of Prognostics and Health Management, vol. 12, no. 2, 2021. (Type: Journal Article | Links | BibTeX)
Zaharah Allah Bukhsh; Nils Jansen; Aaqib Saeed: Damage detection using in-domain and cross-domain transfer learning. In: Neural Computing and Applications, 2021. (Type: Journal Article | Links | BibTeX)
Thom S. Badings; Arnd Hartmanns; Nils Jansen; Marnix Suilen: Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids. In: 13th NASA Formal Methods Symposium, 2021. (Type: Proceedings Article | Links | BibTeX)
Thiago D. Simão; Nils Jansen; Matthijs T. J. Spaan: AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), pp. 1226-1235, IFAAMAS, 2021. (Type: Proceedings Article | Links | BibTeX)
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