## 1 Introduction  
Among all the electric machines used in industrial facilities, induction motors (IMs) are the most widely spread since their ability to transform electrical energy into mechanical energy make them essential for many industrial processes [1]. Additionally, their reliability and robustness results in a low cost and effort related with their maintenance [2; 3]. The IM robustness can be both: a strength and a weakness. It is a strength because the motor is able of continue operating even when it presents a fault condition at the cost of reducing its efficiency [4]; notwithstanding, it can be harmful because a motor working in fault conditions directly affects the power quality and reliability of the entire power system [5], resulting in economic losses and malfunctioning of equipment connected to the same grid. Hence, resulting condition monitoring techniques are helpful for preventing unexpected stops in production processes that may result in losses [6]. In this sense, there are several methodologies that use different physical variables such as current, vibrations, and stray flux to determine the condition of the IM. A well-studied faulty condition is the broken rotor bar (BRB); this condition represents around 15% of the faults presented in the IM [7]. When the BRB condition is presented in the IM, two spectral components appear in the neighborhoods of the fundamental frequency component (FFC) [8]. However, identifying these components is challenging because the energy of the FFC is high compared with the rest of spectral components. The identification is even harder when the IM is powered through a variable frequency drive (VFD) at a low frequency, because the lower the operation frequency is, the closer the failure harmonics get [9]. A similar situation is presented when the IM operates with low load where the sideband harmonics are very close to the FFC, making their detection more difficult [10]. Several techniques have been developed for the BRB detection in IMs, with the motor current signature analysis (MCSA) being the most common one [11; 12; 13]. The MCSA technique uses the fast Fourier transform (FFT) to obtain the spectra from the motor current signals and obtain the amplitude of the fault harmonics. Even though this technique has been used for many years, there are some issues related with their performance, for instance, large amounts of data are required to obtain a proper resolution and, if the FFC is not an integer multiple of the resolution, the resulting leakage can hide the sideband frequencies associated with the BRB condition. In order to deal with these issues, some works have proposed the use of different signal processing techniques like wavelet transform [14; 15], non-uniform time resampling [16], multiple signal classification (MUSIC) [7; 17], among others. Moreover, some other authors propose the analysis of variables different from the current such as sound [18] or vibration [19]. Even though the aforementioned methodologies deal with some of the issues related with the standard MCSA, they present some disadvantages, for instance, wavelet transform suffers from mode mixing and its analysis requires the experience of an expert; MUSIC demands a high computational effort and does not deliver accurate information regarding the fault severity. Additionally, these techniques are commonly used for the analysis of IM operating at a high load, and the case of motors operating at low load is ignored. As aforementioned, when the motor operates at low frequency and the slip detection of a fault condition becomes harder, the failure harmonics become closer to the FFC and the spectral leakage hides them. To deal with this situation, there are works that propose the use of sliding windows to overcome the problems related with the spectral leakage that introduces the Fourier transform [20].