This study proposes a new approach for the detection of broken rotor bar (BRB) fault in three phase induction motor drive using Hilbert transform (HT) and artificial neural networks (ANNs), where the machine is controlled by direct torque control (DTC). HT is preferred to develop the stator current envelope. The sideband frequency and its amplitude of the samples are the input for the ANN. By using fast Fourier transform, the amplitude and frequency components are extracted and the severity of fault is determined by comparing the magnitude of an average of sideband frequency with the fundamental frequency. High accuracy identification of fault is found by ANN, where the results are trained and tested to a minimum mean square error that will detect the number of BRB in the induction motor. DTC is adopted for a suitable control technique in the industrial drives system to maintain good performance in torque control. The performance of the proposed method is verified by using MATLAB/SIMULINK and experimental tests.

中文翻译：

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使用希尔伯特变换和神经网络的转子棒故障检测，应用于感应电动机驱动的直接转矩控制

这项研究提出了一种新的方法，该方法使用希尔伯特变换（HT）和人工神经网络（ANN）检测三相感应电动机驱动器中的转子转子断线（BRB）故障，其中电机由直接转矩控制（DTC）控制。HT是开发定子电流包络的首选。采样的边带频率及其幅度是ANN的输入。通过使用快速傅立叶变换，提取出幅度和频率分量，并通过将边带频率的平均值与基频进行比较来确定故障的严重性。通过ANN可以对故障进行高精度识别，然后对结果进行训练和测试，使其达到最小均方误差，该误差将检测感应电动机中的BRB数量。在工业驱动系统中采用DTC作为合适的控制技术，以保持良好的转矩控制性能。通过使用MATLAB / SIMULINK和实验测试验证了该方法的性能。