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基于振动的日志轴承的磨损状况估计使用卷积自动编码器

Cihan Ates1, Tobias Höfchen1,2, Mario Witt2

  • 1Institute of Thermal Turbomachinery, Karlsruhe Institute of Technology (KIT), 76137 Karlsruhe, Germany.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

本研究引入了一种数据驱动的方法,用于使用振动数据预测期刊轴承磨损. 使用卷积自编码器 (CAE) 的新方法可以准确地估计轴承状况,即使有有限的标记数据.

关键词:
卷积的卷积 卷积的卷积卷积式自动编码器的自动编码器期刊轴承 期刊轴承 期刊轴承机器学习是机器学习.状态空间建模状态空间建模国家轨迹的轨迹.没有监督的学习学习.振动传感器 振动传感器

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