## 5 Discussion  
In this work, an analysis of the stator current and radial stray flux signals for the diagnostics of an induction machine with bearing defects is presented. All the data were collected by performing laboratory tests. The analysis of the signals was obtained through the use of GoogleNet, a deep Convolutional Neural Network (CNN). The network was adapted for the classification of the various bearing fault images through the use of the transfer learning technique. This technique substantially consists of retraining a pre-trained network (i.e., GoogleNet, a network trained for the classification of 1000 classes of object images) only in the last fully connected layers, with a reduction of the number of output classes. The network keeps the feature extraction abilities learned in the first layers in the full training process, but it can adapt these abilities for the classification of new classes of input images (i.e., the time-frequency transformations of the electromagnetic signals coming from the measurements on the machine). The bearing defects considered were generalized roughness (with two severity levels) and localized defects in the outer ring (in four different angular positions). Moreover, tests with both a localized defect and Low Frequency Torque Oscillation (LFTO) were also analyzed. The raw data were converted into a 2D time-frequency domain through the use of the Continuous Wavelet Transform (CWT) and the Short Time Fourier Transform (STFT). Comparison between the data transformed with these two techniques were reported in Section 4. Various types of tests are presented in this paper. The first ones considered three classes, i.e., the baseline (healthy) condition and the faults with generalized roughness. These tests were carried out on both the current and stray flux signals, with the use of the wavelet transform. In these tests, the stray flux signal gave better classification accuracy than the current signal. Other tests were carried out with the addition of the bearing's localized defect data to the baseline and generalized roughness data. With these tests, the stray flux signal gave a validation and test accuracy of 100%, while for the current signal, an accuracy of about 80% was reached but with a maximum number of epochs in the training process augmented to 24. Current and stray flux signals were then mixed together, reaching a validation accuracy of about 90%. In these tests, the data with a full sampling frequency and with a decimated frequency were used, usually reaching better classification accuracy with the non-decimated data. Subsequently, classification tests were carried out with the localized defect data classes with and without the LFTO condition. These tests were performed with the current signal only, due to a lack of data measurement of the stray flux. The tests were carried out with the use of scalogram and spectrogram images, coming from the two different time-frequency domain transformations. Moreover, the double fault condition characterized by the presence of the localized defect in the bearing and the LFTO were analyzed jointly and separately from the single fault condition, that is, the signal of the defected bearing without the torque oscillation. So, the tests with four classes (representing the single or double fault condition of the four localized defect signals) were carried out, and finally the eight classes classification of the single and double faults considered jointly were reported. In these final tests, attention was paid to the differences in classification accuracies of the network with data transformed with the CWT, i.e., the scalogram images, and with the STFT, i.e., the spectrogram images. The results show that the validation and test accuracy can change for the four class setups and for the eight class setups.