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机械预测和高维数据特征提取基于变压器自动注意传输网络

Shilong Sun1,2, Tengyi Peng1,2, Haodong Huang1,2

  • 1Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Shenzhen 518055, China.

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

这项研究引入了变压器自我注意传输网络 (TSTN),用于准确的机械健康预测. 根据高维数据,TSTN方法有效地预测了剩余使用寿命 (RUL),其性能优于现有技术.

关键词:
功能提取 特性提取高维数据的高维数据.预测 预测 预测 预测剩余的使用寿命预测自我注意力传输网络传输网络

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Instrument Transformers01:23

Instrument Transformers

Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...