PFSDS026
Data science based wear prediction
| Grant period | 18 months |
| Funding body | Exploratory Research Space der RWTH Aachen |
| ERS | |
| Identifier | G:(DE-82)EXS-PF-PFSDS026 |
All known publications ...
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Journal Article
Mean-field and kinetic descriptions of neural differential equations
Foundations of data science : FoDS 4(2), 271-298 (2022) [10.3934/fods.2022007]
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Journal Article/Contribution to a conference proceedings
Data-driven wear monitoring for sliding bearings using acoustic emission signals and long short-term memory neural networks
23. International Conference on Wear of Materials, onlineonline, 26 Apr 2021 - 29 Apr 2021
Wear 476, 203616 (2021) [10.1016/j.wear.2021.203616] special issue: "Special issue: 23rd International Conference on Wear of Materials / guest editor: Martin Dienwiebel"
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Journal Article
Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems
Tribology international 155, 106811 (2021) [10.1016/j.triboint.2020.106811]
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Journal Article
Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship
Mechanics of materials 151, 103625 (2020) [10.1016/j.mechmat.2020.103625]
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Contribution to a conference proceedings
Koppelung von KI-Methoden und AVL EXCITE™ Power Unit zur Verschleißprognose von Gleitlagern
AVL Virtual German Simulation Conference 2020 – Proceedings
AVL Virtual German Simulation Conference 2020, onlineonline, 22 Sep 2020 - 24 Sep 2020
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Contribution to a conference proceedings
Machine Learning based condition monitoring and anomaly detection for wear lifetime prediction of journal bearing drivetrains
[TAE: 22nd International Colloquium Tribology - Industrial and Automotive Lubrication]
TAE: 22. International Colloquium Tribology - Industrial and Automotive Lubrication, Meeting location,
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