Traditional maintenance concepts rely on a ‘fixed interval approach”, which takes into account a significant safety margin. As a consequence maintenance is almost always taking place too early or too late, which makes it probably one of the most inefficient industrial activities and most critical at the same time.
Cross-Industry Predictive Maintenance Optimization Platform (CIMPLO)
The CIMPLO-project aims at developing a cross-industry predictive maintenance optimization platform, which addresses the real-world requirements for dynamic, scalable multiple-criteria maintenance scheduling.
Although the system’s capabilities will be demonstrated on our industrial partner’s application cases, namely aircraft engines (KLM) and passenger cars (Honda), it will be developed as a cross-industry platform for generic applications in predictive, multi-objective, dynamic maintenance scheduling. The goal is to provide industry with a tool for dynamically optimizing maintenance activities so as to save time and cost, maximize safety, and optimize asset utilization.
Looptijd: Lopend (t/m 2021)
Mede-projectleiders: Prof. dr. T.H.W. Bdck
Partners: Universiteit Leiden, Faculteit der Wiskunde en Natuurwetenschappen Leiden Institute of Advanced Computer Science (LIACS)
Schrijf u in voor onze nieuwsbrief en blijf op de hoogte van het laatste nieuws over Commit2data