Université de Tunis, Ecole Supérieure des Sciences et Techniques de Tunis, SICISI, 5 avenue Taha Hussein, BP 96, Montfleury, 1008 Tunis, Tunisie.
ISA Trans. 2013 Jan;52(1):140-8. doi: 10.1016/j.isatra.2012.08.003. Epub 2012 Sep 21.
Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph.
通过定子电流信号的高阶谱分析检测和识别感应电机故障，本文提出了一种称为电机电流特征分析（MCSA）的技术。本文提出了两种高阶谱技术，即功率谱和双谱切片，用于分析感应电机定子电流，从而检测转子笼中的电气故障。该方法已通过使用健康和断条转子的情况进行了测试，对于不同负载条件下的 18.5kW-220V/380V-50Hz-2 对极感应电动机。分析实验信号突出表明，双谱结果在准确检测转子断条方面表现出优越性。即使感应电机在低轴负载水平（空载条件）下旋转，转子故障检测也是有效的。我们还将通过分析和实验验证证明，我们提出的方法在接收器操作特性（ROC）曲线和精度召回图方面具有更好的检测性能。
