## 1 Introduction
In [12], the ability of MCSA and stray flux analysis has been evaluated in detecting mechanical faults in industrial cases, concerning induction motors of several hundred kilowatts, supplied at 6 kV; the experimental results of this research revealed that: (i) MCSA is highly sensitive to mechanical faults and is superior to vibration analysis, as well as having low cost and remote monitoring capabilities; (ii) stray flux is insensitive to the mechanical oscillations coming from the load but is more sensitive to the rotor eccentricity and misalignment than MCSA; (iii) stray flux seems unable to detect bearing faults of the entire system consisting of motor, shaft, and pump; (iv) one signal alone (vibration, current, or stray flux) seems unable to provide a comprehensive screening of the electrical machine. Therefore, the authors suggest combining at least two methods among vibration, current, and stray flux analysis. It is worth to noting that the outcomes presented in [12], although very interesting and well supported by experimental measurements in the field, are related to high power motors, and the flux sensors have been installed on the case of the machines, quite distant to the position of the bearings. Hence, it is likely that this type of installation makes it difficult to collect the stray flux containing information about the health of the bearings. It is also important to observe that, in all the papers cited so far, the diagnostic approach was based on the evaluation of the amplitude of specific harmonics of the faults in the spectra of the vibrations or electromagnetic signals; this approach is typical of condition monitoring of the electrical machines, which is complementary to fault diagnosis. In fact, fault diagnostics have been developed by means of these two main approaches: (i) condition monitoring, which is related to the first two stages of diagnostics, i.e., data acquisition and processing; (ii) fault diagnosis, which concerns the last stage, i.e., decision making. Condition monitoring research is aimed at studying the ability of different sensors and processing techniques to identify characteristic signatures in mechanical and electromagnetic signals, which represent the characteristics of specific failures. These characteristics are generally related to the machine parameters (e.g., type of machine, number of poles, number of rotor and stator slots, bearing geometry, etc.) and its working conditions (e.g., speed, load percentage, type of power supply, etc.). Therefore, a deep knowledge of the machine is necessary, not only to identify these signatures, but also to choose the threshold which separates the faulty condition from the healthy condition of the machine, which can heavily influence the probability of committing an error during the decision-making phase. This decision can be made by an individual or by an automatic algorithm. Fault diagnosis research is mainly focused on algorithms able to automatically discriminate the presence of a fault, on the basis of signals coming from the monitored machine. These techniques can be classified in Machine Learning (ML) and Deep Learning (DL); ML techniques include Artificial Neural Network (ANN), Principal Component Analysis, and other methods, which have been known for at least three decades, while DL algorithms have been developed in the last five years [13]. Both ML and DL algorithms require a wide collection of data to be trained, such as datasets available online [14] or extensive laboratory measurement campaigns [15; 16]. But while ML methods rely on human-engineered features in the training process, DL algorithms prove automated feature extraction capabilities and better classification performance [17]. With respect to traditional ANNs, in the branch of the DL, the main difference of the network architectures is that they have more than one hidden layer. A network designed with this criteria, i.e., the Deep Neural Network (DNN), can give outstanding classification accuracy if trained with enough data.