Convolutional autoencoder based condition monitoring system for unique complex technical systems

Abstract This paper presents the development of a condition monitoring system for a unique, complex technical system represented by a drop tower. The self-developed drop tower, the Einstein-Elevator, is designed as a research platform for highly reproducible zero gravity conditions. As the Einstein-...

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主要な著者:	Emre Tahtali, Marco Adamscheck, Ludger Overmeyer, Marc Christopher Wurz, Christoph Lotz, Daniel Klaas
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2025-11-01
シリーズ:	Scientific Reports
主題:	Condition monitoring Technical systems Drop tower Neural network Data augmentation Microgravity
オンライン･アクセス:	https://doi.org/10.1038/s41598-025-26114-w
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