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Automatic detection and classification of rotor cage faults in squirrel cage induction motor

机译：鼠笼感应电动机转子笼故障的自动检测与分类

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摘要

The detection of broken rotor bars and broken end-ring in three-phase squirrel cage induction motors by means of improved decision structure. The structure consists of current signal analysis (CSA), Artificial Neural Network (ANN) and diagnosis algorithm. Effects of broken bars and end-ring on current signal and feature extraction are in the CSA. The rotor cage faults are classified by using ANN. And result matrixes of ANN are considered two different ways for diagnosis. Then the diagnoses are compared with each other. In this study six different rotor faults, which are one, two, three broken bars, bar with high resistance, broken end-ring and healthy rotor, are investigated. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated by analyzing side-bands in current spectrum. To reduce bad effects of changing of distance between the side-band and main component on the detection and classification of the faults, the spectrum is achieved with low definition. Thus, the improved decision structure diagnoses faulted rotors with 100% accuracy and classified rotor faults 98.33% accuracy.
机译：通过改进的决策结构检测三相鼠笼式感应电动机中的转子线棒和端环损坏。该结构包括电流信号分析（CSA），人工神经网络（ANN）和诊断算法。 CSA中包含折线和端环对电流信号和特征提取的影响。使用ANN对转子笼故障进行分类。人工神经网络的结果矩阵被认为是两种不同的诊断方法。然后将诊断相互比较。在这项研究中，研究了六种不同的转子故障，分别是1、2、3条断条，高电阻条，端环断条和转子健康。通过分析电流谱中的边带，与其他故障条件相比，研究了不同转子故障对电流谱的影响。为了减少边带与主要部件之间的距离变化对故障的检测和分类的不良影响，以低清晰度实现了频谱。因此，改进的决策结构以100％的精度诊断故障转子，并以98.33％的精度对转子故障进行分类。

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