Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Taal, Cees"'
Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost, placement constraints, or inability to directly measure certain criti
Externí odkaz:
http://arxiv.org/abs/2407.18691
Publikováno v:
Vol. 8 No. 1 (2024): Proceedings of the European Conference of the PHM Society 2024 Technical Papers
Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load mo
Externí odkaz:
http://arxiv.org/abs/2404.02304
In the presented work, we propose to apply the framework of graph neural networks (GNNs) to predict the dynamics of a rolling element bearing. This approach offers generalizability and interpretability, having the potential for scalable use in real-t
Externí odkaz:
http://arxiv.org/abs/2309.10418
Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore,
Externí odkaz:
http://arxiv.org/abs/2302.01704
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis method
Externí odkaz:
http://arxiv.org/abs/2112.07356
Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of la
Externí odkaz:
http://arxiv.org/abs/2107.01849
Publikováno v:
In Reliability Engineering and System Safety February 2024 242
Publikováno v:
PHM Society European Conference. 7:306-314
Annotations in condition monitoring systems contain information regarding asset history and fault characteristics in the form of unstructured text that could, if unlocked, be used for intelligent fault diagnosis. However, processing these annotations
Publikováno v:
In Computer Speech & Language July 2014 28(4):858-872
Publikováno v:
IEEE Transactions on Instrumentation and Measurement, 71
Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a771380d154a2ed1249ea4fea144c5aa