Real time application of artificial neural network for incipient fault detection of induction machines

M. Chow, S. Yee
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引用次数: 15

Abstract

This paper describes several artificial neural network architectures for real time application in incipient fault detection of induction machines. The artificial neural networks perform the fault detection in real time, based on direct measurements from the motor, and no rigorous mathematical model of the motor is needed. Different approaches used to develop a reliable fault detector are presented and compared in this paper. The designed networks vary in complexity and accuracy. A high-order fault detector neural network is discussed first. Then noise considerations are included in more complex fault detector models, since noise is an important factor in the design and analysis of real time fault detector neural networks. Simulation results show that with appropriate designs, artificial neural networks perform satisfactorily in real time incipient fault detection of induction machines.
人工神经网络在感应电机早期故障检测中的实时应用
本文介绍了几种用于感应电机早期故障实时检测的人工神经网络结构。人工神经网络基于电机的直接测量实时进行故障检测,不需要电机的严格数学模型。本文介绍并比较了用于开发可靠故障检测器的不同方法。所设计的网络在复杂性和准确性上各不相同。首先讨论了一种高阶故障检测神经网络。然后在更复杂的故障检测模型中考虑噪声因素,因为噪声是实时故障检测神经网络设计和分析的重要因素。仿真结果表明,通过适当的设计,人工神经网络在感应电机的实时早期故障检测中取得了令人满意的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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