## 2 Background  
Much research effort in the field of RUL prediction has been made in the last decade. Although there is no standardized procedure, most of the related works divide RULprediction into four steps: feature extraction, health indicator construction, health stage division, and RUL prediction. The first step consists of gathering data from the monitored machinery. IIoT has facilitated this task using wireless sensor networks composed of different types of sensors. Vibrations [20; 21], acoustic emissions [22; 23], temperatures [24], and current measurements [25; 26] are among the most commonly used data in the RUL prediction of electric motors. However, gathering run-to-failure data from running machinery for training the prognostic model is unfeasible because a run-to-failure breakdown may cause the complete breakdown of the machinery with unpredictable consequences [8]. In order to build prognostic models, there are public datasets that contain data acquired from accelerated degradation tests. There are several publicly available run-to-failure datasets of electric motors. The most commonly used in health prognostics of electric motors are the FEMTO-bearing dataset and the IMS-bearing dataset [17; 18]. In the second step, health indicators (HIs) are constructed in order to predict the health status of the monitored machinery. They can be of two types depending on the technique used to construct them: physical HIs (PHIs) and virtual HIs (VHIs) [8]. PHIs are health indicators that are based on the physical properties of the monitored machinery, such as vibration or temperature. They can also be determined by a single feature extracted from monitored signals using signal processing techniques, such as wavelets [24] and fast Fourier transforms (FFTs) [26], or statistical methods, such as RMS, Kurtosis, and skewness [20; 22; 26]. VHIs are health indicators derived from data collected from the monitored machinery and are related to physical properties but are not necessarily measurements of those properties. They can be used to supplement or replace PHIs, and are more convenient and cost-effective to obtain in some cases. Techniques commonly used to fuse features and reduce their dimensionality are the principal component analysis (PCA) [27; 28], stacked autoencoders (SAEs) [29], Mahalanobis distance [30], and Kalman filters [21; 25], as well as artificial intelligence techniques, such as fuzzy networks [31], convolutional neural networks (CNNs) [32], and recurrent neural networks (RNNs) [33; 34; 35]. The third step comprises the HI stage division. Depending on the trend of the HI curve, this can be split into multiple stages. Each of these stages provides boundaries or points for predicting RUL [36]. Many studies divide the HI curve into two stages, which are the normal operation condition and failure condition [37]. However, other works, in which the slope of the HI curve is less pronounced, divide the HI curve into four stages: normal, mild degradation, moderate degradation, and near-to-failure [38]. The last step of health prognostics is the actual RUL prediction. There are different types of RUL prediction models depending on the type of data, methodology, algorithm, or even previous knowledge about the machinery to be analyzed [39]. Physics-based approaches use physical laws and principles to model the behavior of the monitored machinery over time. They provide the highest accuracy and precision, albeit with the disadvantage that they require expert knowledge to perform an in-depth analysis of the training dataset. These models have to be developed from scratch, thus limiting reusability with other similar datasets [40; 41].