## 1 Introduction  
The evolution of industrial technology gives rise to a demand for more precise and effective machinery. This imposes higher reliability and safety constraints on the electric motors used in many industrial systems, such as pumps, turbines, and mills. Electric motor failure is common due to the motor's continuous use and the conditions that many motors must withstand. The percentage breakdown of different types of motor failures is presented in various studies [1; 2; 3; 4]. According to these studies, bearings are the most common types of motor failure, being between 40% and 50% of failures. Stator failures come in second, with approximately 30-35%. The third most common failures are rotor failures, by around 10%. All other types of failures make up the remaining percentage. Bearing failures can occur at different locations, such as in the outer race, the inner race, and the rolling elements or the bearing cage. Maintenance has become one of the most important tasks in Industry 4.0, not only because it increases the availability of devices but also because it increases productivity. Maintenance in Industry 4.0 accounts for 15-60% of total production costs [5]. Thereare three different types of maintenance according to when it is performed: reactive maintenance, which is conducted when the device fails; preventive maintenance, which is carried out during planned maintenance shutdowns; and predictive maintenance, which is performed before the device is expected to fail [6]. The use of predictive maintenance means a reduction of 50-60% in maintenance costs and equipment damage, while the machine life and productivity are also increased by 50-60% and 20-30% respectively [7]. However, this type of maintenance is difficult to perform due to the need for constant monitoring of devices. Health prognostics are essential in predictive maintenance. It focuses on predicting the remaining useful lifetime (RUL) of machinery by studying and analyzing their historical and continuing degradation trends based on data gathered by sensors [8]. Some authors refer to predictive maintenance as prescriptive maintenance when, in addition to predicting the RUL, it is also possible to identify the type of failure [9]. RUL prediction is usually carried out in four steps: feature extraction, health indicator construction, health stage division, and RUL prediction using a trained machine learning model. The first step involves selecting which features are relevant, collecting them during the operational lifetime of the machinery, and preprocessing and filtering them. The second step is the compilation of the health indicator (HI). HIs are measurements or characteristics that provide information about the condition or state of the monitored device. They can either be physical (obtained from a single feature) or virtual (obtained from a fusion of various features). The third step is performed once the curve with all values of the HI during the motor full operation cycle is obtained. It consists of dividing the HI curve into two or more stages according to the degree of degradation of the machinery. These stages can be healthy, when no degradation is observed or damaged, or when a degradation trend appears. Finally, when the HI signals a degradation state, the last step uses a machine learning model trained with run-to-failure data to predict how much life (usually cycles) of the electric motor is left until failure occurs [10; 11]. The Industrial Internet of Things (IIoT) facilitates prognostics thanks to the use of sensor networks that monitor the condition of electric motors, gathering data about vibrations, temperature, and other important features [12; 13]. Most of the failures in electric motors are due to bearings [1; 2; 3; 4]. Most motor bearing failures manifest themselves differently [14] and can be detected using measurable signals, such as vibrations, stator currents, thermographs, and noise [15].