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Open and closed-loop motor control system with incipient broken rotor bar fault detection using current signature

机译:开环和闭环电机控制系统,利用电流信号检测转子棒早期断裂

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Motor drive system is considered the most important asset in industrial applications. Detection of broken rotor bars has long been important but difficult job in detection area of incipient motor faults. The need for highly efficient motor control drive systems becomes more and more important. Motors are controlled in closed-loop or open-loop modes of operation. This paper develops a novel approach for fault-detection scheme of broken rotor bar faults for three-phase induction motor using stator current signal. The empirical mode decomposition (EMD) combined with Wigner-Ville distribution (WVD) has been employed for the analysis of stator current signal. Artificial neural network is then used for pattern recognition of broken rotor bar signature. The proposed algorithm offers high performance in detecting broken rotor bar fault. Both simulation and experimental results show that stator current-based monitoring in conjunction with Winger-Ville distribution based on EMD yields a reliable indicator for detection and diagnosis of broken rotor bar faults using artificial neural network. All simulations in this paper are conducted using finite element analysis software. Experimental results validate the simulation and analytical results.
机译:电机驱动系统被认为是工业应用中最重要的资产。转子条断裂的检测一直很重要,但在电动机初发故障的检测领域却是一项艰巨的工作。对高效电机控制驱动系统的需求变得越来越重要。电机以闭环或开环操作模式进行控制。本文提出了一种基于定子电流信号的三相感应电动机转子断条故障检测方案。经验模态分解(EMD)与Wigner-Ville分布(WVD)相结合已用于分析定子电流信号。然后,将人工神经网络用于损坏的转子条签名的模式识别。所提出的算法在检测转子断条故障方面具有很高的性能。仿真和实验结果均表明,基于定子电流的监测以及基于EMD的Winger-Ville分布可为使用人工神经网络检测和诊断转子断条故障提供可靠的指标。本文中的所有模拟都是使用有限元分析软件进行的。实验结果验证了仿真和分析结果。

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