Fault diagnosis and testing of induction machine using Back Propagation Neural Network

N. Rajeswaran, T. Madhu, M. Kalavathi
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引用次数: 5

Abstract

The recent developments with AI (Artificial Intelligence) are extremely intricate and are useful in a wide range of domestic and industrial applications. In real time environment, operating the induction motor at variable speeds is a severe constraint. The electrical and mechanical faults can impose unacceptable conditions and protective devices are therefore provided to quickly disconnect the motor from grid. In order to ensure that electrical machines receive adequate protection, extensive testing is performed to verify the high quality of assembly. Fault diagnosis and testing of induction machine is attempted under various load conditions and verified by using Field Programmable Gate Array (FPGA). Back Propagation Neural (BPN) Network is used to calculate the error and correct/regulate the induction motor. This technique has resulted in increased speed and improved fault coverage area of the induction machine.
基于反向传播神经网络的感应电机故障诊断与检测
AI(人工智能)的最新发展非常复杂,在广泛的家庭和工业应用中都很有用。在实时环境下,异步电动机的变速运行是一个严重的限制。电气和机械故障可能会造成不可接受的情况,因此提供保护装置以快速断开电动机与电网的连接。为了确保电机得到足够的保护,进行了广泛的测试,以验证组装的高质量。利用现场可编程门阵列(FPGA)对感应电机在各种负载条件下的故障诊断和测试进行了尝试,并进行了验证。采用反向传播神经网络(BPN)计算误差并对异步电动机进行校正/调节。这种技术提高了感应电机的速度,改善了故障覆盖面积。
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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