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A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

机译:使用MCSA,DWT和EMD技术进行感应电动机转子故障检测的对比研究

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This paper presents the detailed detection of broken rotor bar faults in squirrel cage induction motors (SCIMs). This study used three diagnostic techniques. Early faults detection allows us to avoid catastrophic damage. In this work, we have exploited the stator current signal by three recent methods. The first technique uses the fast Fourier transform (FFT) which is often called motor current signature analysis (MCSA or MCSA-FFT). According to this technique, we carefully checked the spectral content of the stator current. In addition, we have clearly noticed to new harmonics that indicate the broken rotor bar (BRB) faults exists. The second technique is based on the discrete wavelet transform (DWT); this technique is widely used in the diagnosis field of rotating machinery. In order to detect the BRB faults in SCIMs, we have exploited this method by three important indicators. One of them is based on the mean square error (MSE) of each detail coefficient. In this study, we applied a new indicator (MSE) for the BRB fault detection based on DWT. The last method is empirical mode decomposition (EMD) to perform the current signature analysis in order to decompose the motor current signal into intrinsic mode functions (IMFs). It is currently competing with several methods such as: MCSA, DWT, etc. So, it is possible to detect BRBs through the evaluation of the different IMF levels for both conditions, healthy and faulty state of SCIM. An experimental test for different conditions: at no load or at load operation, healthy or faulty state of the induction motor has been performed. So, experimental results using three methods showed a detailed comparison between them in order to achieve a judicious decision on the broken rotor bars detection. Finally, we have confirmed the proposals ideas in this subject in order to detect the broken rotor bar faults.
机译:本文介绍了鼠笼式感应电动机(SCIM)中转子条损坏的详细检测方法。这项研究使用了三种诊断技术。早期的故障检测使我们避免了灾难性的破坏。在这项工作中,我们通过三种最新方法利用了定子电流信号。第一种技术使用快速傅立叶变换(FFT),通常被称为电动机电流信号分析(MCSA或MCSA-FFT)。根据这项技术,我们仔细检查了定子电流的频谱含量。另外,我们已经清楚地注意到新的谐波,表明存在损坏的转子条(BRB)故障。第二种技术基于离散小波变换(DWT)。该技术广泛应用于旋转机械的诊断领域。为了检测SCIM中的BRB故障,我们通过三个重要指标来利用此方法。其中之一是基于每个详细系数的均方误差(MSE)。在这项研究中,我们为基于DWT的BRB故障检测应用了新的指标(MSE)。最后一种方法是经验模式分解(EMD),以执行电流签名分析,以便将电动机电流信号分解为固有模式函数(IMF)。目前,它正在与多种方法竞争,例如:MCSA,DWT等。因此,有可能通过评估SCIM正常和故障状态的不同IMF级别来检测BRB。针对不同条件的实验测试:空载或负载运行时,感应电动机处于健康或故障状态。因此,使用三种方法的实验结果显示了它们之间的详细比较,以便对转子条的断裂检测做出明智的决定。最后,我们已经确认了本主题中的建议思想,以便检测损坏的转子棒故障。

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