## 1 Introduction  
Rolling bearings are purely mechanical elements; nevertheless, they represent a critical factor for the safe operation of rotating electrical machines. In fact, if their maintenance is disregarded, i.e., if they are not correctly lubricated and/or substituted in due time, they can fail, causing the breakdown of the electrical machine. Moreover, a harsh environment with pollutants, humidity, and high temperature can accelerate bearing wear. Last but not least, the power supply of electrical motors by means of electronic converters can arouse shaft voltage and bearing currents if the bearing is not properly insulated, with a consequent early fault of the bearing itself [1]. For these reasons, and considering that bearing faults represent more than 40% of induction motor faults [2, 3], for the last several decades, much attention has been focused on the condition monitoring of rolling bearings, initially based on vibration measurements and more recently on the measurement of electromagnetic signals coming from the electrical machine in which they are installed, i.e., stator current and external stray flux. Bearing diagnostics through vibration analysis is mainly based on the evaluation of harmonics at specific frequencies, depending on which surface of the bearing contains the fault (outer race, inner race, rolling element, cage), on the geometrical dimensions of the bearing, and on the rotational speed of the rotor [3]. This technique is effective if the fault is localized (single-point), while in the case of generalized roughness, i.e., when the fault is spread over the entire bearing, other algorithms, like envelope analysis, have to be applied for processing the collected vibrations [4]. Vibration measurements require the installation of additional sensors, such as accelerometers, while current and stray flux measurements can take advantage of the sensors already present in the electrical drive to control of the motor. For this reason, since 1995 [5], many studies have investigated the potential of analyzing the current as an alternative diagnostic signal for bearing fault detection and, since 2006 [6], the external stray flux has also been considered for the same purpose. The research group of this paper has previously investigated the advantages offered by electromagnetic signal analysis for bearing diagnostics, in particular, Motor Current Signature Analysis (MCSA) for single-point defects and generalized roughness [7], stray flux analysis for single-point defects [8], and both signals for identifying different steps of generalized roughness [9]. More recently, electromagnetic signals proved to be efficient in detecting bearing defects and other mechanical faults. In particular, in [10], two fault severities in the rolling elements and cage of a bearing were recognized via MCSA in an induction motor of 5.5 kW, while in [11], an inner race fault was identified by means of vibration, current, and stray flux analysis in an induction motor of 4 kW. From the comparative study presented in [11], the stray flux seems to provide the best results, as it is insensitive to external vibrations of the motor; nevertheless, the authors suggest using this signal in combination with at least one of the other signals, to increase the reliability of the diagnosis.