## 1 Introduction
#### Paper Structure  
Stray flux and current measurements were used in this work, performed on an induction motor. The measurements were done in both the conditions of healthy motor and different bearing faults. The bearing faults considered were the following: two types of generalized roughness (\"step 1\" and \"step 2\" roughness, as described below) and a single-point defect in the outer ring in four different positions.  
Moreover, in order to test our method in a more difficult condition, the Low Frequency Torque Oscillation (LFTO) was added in some cases (double fault condition). The experimental setup is described in Section 3.  
The measured signals were processed with time-frequency transformations; for the sake of a comparison, either the Continuous Wavelet Transform (CWT) or the Short Time Fourier Transform (STFT) were used. In particular, the CWT generated the so-called \"scalograms\", while the STFT generated classical \"spectrograms\". The methods used for the time-frequency transformations are detailed in Section 2.1.  
In order to estimate the effect of a different sampling frequency of the measurements on the fault detection capability, the time-frequency transformation with both the CWT and STFT was also performed for the so-called \"decimated data\", i.e., data with a lower acquisition frequency.  
The images obtained (scalograms or spectrograms, decimated or non-decimated) were elaborated by a Deep Convolutional Neural Network, GoogLeNet [30].  
The CNN GoogLeNet is trained in this work for classification purposes by means of the transfer learning technique. In fact, GoogLeNet is a pretrained net; the use of a pretrained net is necessary when the number of samples for training the net is small, as it was in this case. The CNN approach used in the paper is presented in Section 2.2.  
Multi-class classification of faults was considered in this work, namely:  
* three classes (healthy, generalized roughness step 1, generalized roughness step 2) in Section 4.1;
* three classes (as before) and decimated data in Section 4.1.1;
* seven classes (three classes as in Section 4.1 and four classes relevant to a localized crack in the outer ring in four different angular positions) in Section 4.2; in particular, only flux measurements, only current measurements, and both kinds of measurementswere investigated in Sections 4.2.1-4.2.3, respectively; moreover, in Section 4.2.3, a comparison between decimated and non-decimated data is shown;
* four classes (relevant to a localized crack in the outer ring in four different angular positions) with and without the presence of the LFTO in Section 4.3.1;
* eight classes and LFTO (four classes relevant to a localized crack in the outer ring in four different angular positions and four classes for the same faults but with the LFTO added) in Section 4.3.2. All the previous cases were implemented with GoogLeNet fed by scalograms. In Sections 4.4.1 and 4.4.2, the same classes as those used in Sections 4.3.1 and 4.3.2 were considered, but the SFTF (spectrograms) was used instead of the CWT (scalograms) for the time-frequency transformation. Moreover, decimated data were considered. Finally, Sections 5 and 6 report a final discussion and the conclusion of the overall work.