## 1 Introduction
Besides the classification accuracy, other favorable characteristics of this kind of network are the automatic feature extraction during the training process and greater ease to adapt the network to other purposes, with the transfer learning technique [17]. In the DL context, Convolutional Neural Networks (CNNs) gained popularity because of their shared weights topology which allows them to delve deeper, thereby achieving very good classification accuracy, with fewer parameters required for training than a fully connected topology [18]. A very thorough and detailed review on DL for bearing fault diagnostics was published last year [17]; therefore, the analysis of the state-of-the-art research presented here will be limited to a few recent papers. In [19], the publicly available dataset of the Case Western Reserve University (CWRU) [20] was used to train a DNN. In this case, the time-domain signals were transformed into the frequency domain and features were extracted from the spectra to reduce the high data dimension to a small dimension before entering the data into the DNN for training; in this way, the dataset was not based on an expert or previous knowledge, as in traditional ANN. In [21], the CNN AlexNet was modified by only replacing the last fully connected layer; the raw acceleration signals were converted into uniformly sized time-frequency images, even when the data had different sizes. The proposed method was tested on both experimental measurements carried out in the laboratory and CWRU dataset. In [22], a transfer learning CNN based on AlexNet was proposed for bearing fault diagnosis, wherein a 2D image representation method converts vibration signals to 2D time-frequency images. Then, the proposed CNN model extracts the features of the 2D time-frequency images and achieves the classification conditions of the bearing, which is faster to train and more accurate. The bearing data from CWRU was used to verify the performance of the proposed method. The work presented in [23] is particularly interesting because it tested the validity of a CNN on both test bench data and actual service data, measured on a locomotive running on the real line. The faults considered were in the inner ring, outer ring, and roller of a bearing. In [24], a CNN model based on the AlexNet was proposed to classify the wear level of bearings, by using a dataset from the center of Intelligent Maintenance Systems. Even the authors of the present paper have already considered two pre-trained networks for diagnostic purposes [25]. Other recent works have investigated further variants of these networks, named \"Deep Residual\", and tested them on measurements from experimental test benches in the laboratory [26; 27]. It is evident that fault diagnostics of electrical machines implies the acquisition of several measurements from the machine and a post-elaboration of the data for diagnosis purposes. To this end, the use of automatic DL methods is becoming more and more useful and effective. In particular, CNNs are usually applied with the aim of classification of faults. In order to have good accuracy, a CNN should be well trained, with a lot of different samples, measured from different machines and in various operating conditions. This is obviously not always possible, due to the dangers that arise from running a faulty machine and the high number of different operating conditions (speeds and load) [17]. An approach which showed good accuracy while allowing for a small number of samples is based on pre-trained networks [21; 22; 24]. Other works use the transfer learning technique for bearing diagnosis, such as with a 1D-CNN taken from a ResNet model in [28] and with a GoogLeNet model and 2D signal transformation in [29]. However, both papers only considered vibration and not electromagnetic signals.