## 2 Materials and Methods  
In this work, time-frequency domain images obtained from signals measured on an induction motor with bearings with different types of faults were used as input for a CNN. In particular, the signals from the current sensor and the radial external stray flux sensor of an induction motor were transformed into the time-frequency domain via the Continuous Wavelet Transform (CWT), which generates scalograms; for comparison, transformations with the Short Time Fourier Transform (STFT), which produces graphs called spectrograms, are also presented. The choice to use the wavelet transform arose from the fact that many papers have recently used it for the diagnostics of electrical machines, such as in [31]. Regarding bearing defects in particular, interesting findings can be found in [32; 33], where a Discrete Wavelet Transform (DWT) and a CWT were used on vibration signals for the diagnosis of bearing defects. In [34], a wavelet packet decomposition and a Hilbert envelope were used on the motor current signal for bearing fault detection. Other works dealing with Wavelet Transforms for the diagnosis of induction machines include [35; 36; 37]. Lastly, in [38], an analysis of the motor current signal through a CWT is presented. To the knowledge of the authors of this paper, there are currently no publications available demonstrating wavelet analysis of machines' stray flux for bearing fault recognition. The signals analyzed came from an induction motor fed by an inverter, which implies more difficulties in the classification of the faults as stated in various works throughout the literature, such as in [39; 40]. One of the prevailing difficulties lies in the fact that the frequency of the current waveform could be variable for the speed control of the machine, and this can lead to transients in which the characteristic harmonics of the sought faults cannot be correctly identified with a normal frequency-domain analysis. Another important difficulty is that the electronic commutations carried out in the inverter for the generation of the waveform determine the formation of Electromagnetic Interference (EMI), which could easily cover the characteristic fault harmonics. The last main difficulty in searching for faults in electronic-fed drives is the presence of a closed-loop control, especially used in Field Oriented Control (FOC) drives. In fact, as stated in [41], while it could still be possible to see fault harmonics with an open loop V/f controlled driver, with a FOC closed-loop it is not possible to diagnose bearing faults via normal current and voltage measurements (without any change on the FOC structure). Many techniques have been implemented in various works to reduce these effects on the diagnostic system, especially for the detection of bearing faults that have very low characteristic harmonic amplitudes, for incipient faults in particular. It is often stated in the literature that the analysis of vibration signals could be the best choice for the diagnosis of the bearings, because the bearings' mechanical faults generate a change in the signature of the vibrations as a primary effect. The harmonics in the current or stray flux spectra come from secondary effects of the fault, i.e., change in the air-gap distribution (air-gap eccentricity) and introduction of torque ripples due to the fault in the bearing [5; 42]. Besides this, vibration measurements could be difficult in some cases because the machine could not be easily accessible and the costs of purchase and maintenance of the sensors could be high and increase the complexity of the system. Moreover, the vibration signature could change from one physical point of the machine to another; for example, it could be necessary to install more than one accelerometer to check both the fan-end and drive-end bearings.