A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series

Cheng Cheng; Ding Jia; Zhang Yong

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A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series

机译：利用和低维工业时间系列机械健康监测和预测的北斯诺曼操作员方法

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《Neurocomputing》 |2020年第17期|204-214|共11页
作者
Cheng Cheng; Ding Jia; Zhang Yong;
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作者单位

Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Intelligent Control & Image Proc Wuhan 430074 Peoples R China;

Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China;

Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
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4. Approximating the Koopman Operator using Noisy Data: Noise-Resilient Extended Dynamic Mode Decomposition [C] . Masih Haseli, Jorge Cortés Annual American Control Conference . 2019

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5. Online monitoring and prediction of complex time series events from nonstationary time series data. [D] . Wang, Shouyi. 2012

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A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series

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