基于AI的智能EOCR电机系统状态鉴别器

Kyung-Min Lee, Chul-Won Park
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引用次数: 0

摘要

近年来,人们对应用AI(人工智能)技术预测电机缺陷、进行预防和维护、降低恢复成本和损失越来越感兴趣。本文提出了一种基于ai的电机系统状态鉴别器,通过加入CLOUD环境的预测性维护功能,将现有的EOCR改进为智能EOCR。首先,介绍了基于EOCR的智能电机系统。其次,构建了从运动系统中收集的五个状态学习数据集。利用广泛应用的人工智能技术DNN (Deep Neural Network)设计了一个状态鉴别器,并使用Python语言实现。我们证明了状态鉴别器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Based State Discriminator of Motor System for Smart EOCR
Recently, there has been growing interest in applying AI (artificial intelligence) technology to predict electric motor defect, perform prevention and maintenance, and reduce recovery costs and losses. This paper proposes an AI-based state discriminator for the electric motor system to improve the existing EOCR into a smart EOCR by adding the predictive maintenance function of the CLOUD environment. Firstly, the smart EOCR based motor system is introduced. Next, five state learning data sets collected from the motor system are constructed. After designing a state discriminator with DNN (Deep Neural Network), a widely used AI technique, and implementing it using the Python language. We prove the effectiveness of the state discriminator.
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