## 1 Introduction
This work considers that the amplitude of the FFC fluctuates due to the existence of sidebands frequencies, and with the use of different windows, it is possible to appreciate these variations and relate them to the fault conditions. Notwithstanding, the results are highly dependent on the selected window, therefore, certain experience is required in the response of different windows. Moreover, the math behind the implementation is more complex than the one proposed by other methodologies. Another well-studied alternative is the use of the magnetic flux instead of the current signals [21; 22; 23]. These works consider that when a fault appears in the IM, a magnetic unbalance can be appreciated, and a portion of the magnetic flux is radiated outside the frame of the machine. However, the measured signal is very weak and can be affected by the surrounding noise in an easy way, compromising the accuracy of the results. On the other hand, the Hilbert transform is an effective tool that allows computing the instantaneous frequency of the current signal [24; 25]. Notwithstanding, since the major problem in the detection of BRB is that the energy of the FFC is so high compared with the rest of spectral components, a more common solution is the use of techniques for suppressing the FFC from the current signals. Herein, the most explored technique is the use of notch filters (NFs) for attenuating a very reduced frequency bandwidth [26; 27; 28; 29]. These works use the NF as a pre-processing stage in order to enhance the results delivered by the common methodologies. The problem with the use of NFs is that they suppress a complete frequency band instead of only removing a single frequency; therefore, if the frequency band is not narrow enough, the failure harmonics may be attenuated too. In this sense, developing a technique that is able to suppress a single frequency instead of a frequency band may improve the results in the detection of faulty conditions in the IM as BRB. Therefore, in [30], the use of the Teager-Kaiser energy operator is proposed for eliminating the FFC and obtaining the fault harmonics right at their characteristic frequencies, instead of bands around the FFC. However, the case when the IM operates at 50 and 60 Hz is only studied, and it is necessary to show what happens when the motor works at lower frequencies. Finally, it is important to mention that artificial intelligence techniques have been recently used as tools for condition monitoring of IM, with genetic algorithms (GAs), artificial neural networks (ANNs), and fuzzy logic being some of the reported techniques [3; 31; 32; 33]. The use of artificial intelligence techniques allows to perform an automatic detection of any fault condition in the IM; however, they require a training process and large amounts of data to properly work. The amount of data and the training process could be reduced if a GA is used as a pre-processing stage that works, along with the conventional MCSA technique. A study like this is presented in [34], where a GA is used for suppressing the FFC in order to improve the detection of the broken bar condition in induction motors. However, the reported results consider only one operation frequency for the IM and no comparison with other methodologies is performed. In order to demonstrate that the results are reliable and that a significant improvement against other techniques is reached, it is necessary to carry out tests under different conditions and perform a fair comparison with other methodologies. This work proposes a technique that allows the estimation of a single frequency, amplitude, and phase to define the FFC. The novelty of this work consists on the development of a preprocessing technique that estimates the parameters of the FFC as an optimization problem. Therefore, a GA is used for this task, allowing to deal with some of the issues related with other methodologies. For instance, the proposed methodology suppresses a single frequency component instead of removing a whole frequency band as the NF does.