Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors  
[PERSON]  
1 Department of Electromechanics, Federal Institute of Parana, Av. [PERSON], s/n,  
Jacarezinfo, PR 86400-000, Brazil; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Electrical Engineering, Federal University of Technology-Parana, Av. [PERSON], 1640,  
Cornello Procopio, PR 86300-000, Brazil; [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (M.F.C. ); [EMAIL_ADDRESS] (W.F.G.) [PERSON]  
2 Department of Electrical Engineering, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, Spain; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Electrical Engineering, Federal University of Technology-Parana, Av. [PERSON], 1640,  
Cornello Procopio, PR 86300-000, Brazil; [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (M.F.C. ); [EMAIL_ADDRESS] (W.F.G.) [PERSON]  
3 ADIRE-ITAP-Department of Electrical Engineering, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, Spain; daniel. power or magnetic signals, and oil or waste analysis [3]. However, most of these methodologies require invasive sensors that make their application difficult, requiring access to the machines' interior, which is difficult during operation. Thus, several approaches based on the stator current have recently received great attention, as it is a non-invasive method and is considered a reliable technology. The current sensor has advantages such as low-cost, simple access, ease of implementation, and it does not require additional sensors [3]. Signal processing tools have helped monitor induction motors, as they allow the extraction and selection of the most relevant characteristics of the signals. In the literature, conventional methodologies work in the frequency domain, which uses spectral decomposition of signals, such as Fourier Transform (FT) [1; 2; 3; 4; 5; 6], the Wavelet Transform (WT) [3; 7; 8], and the Hilbert Transform (HT) [9]. Some researchers are recently employing advanced tools to extract the relevant fault signatures for diagnosing failures in electrical machines. In [PERSON] et al. [7], a methodology for the diagnosis of bearing defects based on Optimized Standing Wave Packet Transformation (Op-SWPT) and the artificial immune network (aiNet) is presented. The Op-SWPT allows extracting the characteristics of the defects from current signals even with a low acquisition rate and a reduced number of samples. In [PERSON] et al. [10], the authors used linear discriminant analysis (LDA) and neural networks (NN) for monitoring multiple failures in TIMs; the main objective was to present a methodology for reducing the size of the data to increase the performance of the diagnostic system. Thus, allied to LDA, the researchers used other optimization tools such as the genetic algorithm, principal component analysis (PCA), and [PERSON]'s scoring technique to select the most significant attributes resulting from the vibration decomposition signals. This set of tools allowed using a simple artificial neural network (ANN) to identify multiple faults. Recently, the work of [PERSON] et al. [11] presents an alternative method based on envelope kurtosis for the selection of intrinsic mode functions (IMFs) generated by Value Mode Decomposition (VMD). The proposed methodology allows selecting the IMFs that contain the most relevant fault information. The results obtained showed that kurtosis outperforms other statistical measurements, such as the cross-correlation coefficient and entropy. In [PERSON] et al. [12], the researchers applied an adaptive filter based on nonlinear stochastic resonance (SRAF) to diagnose the bearings' defects of direct current motors. This tool filters out acoustic signals from the machine, reducing noise interference and improving the characteristic of defect-induced signatures. The proposed methodology also uses the zero-crossing detection algorithm to directly measure the signal period, identifying the bearing defect type.