## 1 Introduction
In a different approach, reference [14] shows the use of statistical features to identify between OR, IR, and ball defects in bearings by using a Random Forest Algorithm (RFA), obtaining results of 99.57% in the classification for the one-severity faults. These methods can detect the presence of faults, and some can differentiate between a couple of faulty states with only one severity; however, most systems require significant amounts of information to discriminate between a more extensive set of faults or severities. In that regard, data fusion from different sensors is a trend in condition monitoring as the measurement of multiple variables allows the information of one sensor or physical magnitude to be complemented with other sources to facilitate the diagnosis, sometimes with the requirement of more robust analysis techniques to manage the considerable amounts of data produced [15; 16]; as an example, the authors of [17] implemented a multi-sensor system using an accelerometer and a load cell to diagnose different bearing conditions using time and frequency indices, fusing the decisions of a k-Nearest Neighbors (kNN) classifier, showing an improvement in diagnosis when combining the decisions of the classifier for each sensor. A different approach was published in [18], where the researchers utilized Transfer Learning to generate a complex network capable of the fusion of information from different domains, being capable of identifying OR, IR, and ball defects with 95.97% to 98.31% accuracy. The focus of [19] was the analysis and comparison of features in time, frequency, and time-frequency domains to identify the best option to diagnose OR defects in a ball bearing through the measurement of current and vibration signals, showing better results with the application of the Hilbert-Huang Transform (HHT). Similarly, in reference [20], acoustic emission (AE) and vibration signals are used to identify the presence of an OR defect under various speeds and load levels, showing better results in fault identification when combining the Root Mean Square (RMS) values of the vibration signals and the amplitude of the AE signals. The diagnosis of multiple simultaneous faults has also been studied, showing interesting alternatives to the detection of combined defects. In this area, works like [21] implemented a vibration-based system to detect sixteen defect conditions in bearings consisting of the combinations of OR, IR, ball, and cage damages simulated from single fault signals using DWT, Hilbert Transform (HT), statistical features, metaheuristic algorithms and comparing the results of a Feed-Forward Back-Propagation (FFBP) Artificial Neural Network (ANN), a Support Vector Machine (SVM) with a Gaussian kernel, and a kNN classifier, resulting in a maximum outcome of 91.6% effectiveness from the ANN. In reference [22], a wavelet-based moving average control chart is studied to diagnose the combination of OR, IR, and rolling element faults using vibration signals and comparing the results to those obtained with Empirical Mode Decomposition (EMD) and Hilbert envelope spectrum analysis, showing positive results with the method and also revealing the difficulty of identifying the faults if the signals have similar energy dispersion patterns. As previously established, lubricant contamination is a primary cause of defects in bearings; the first studies to investigate the effect of contaminants were carried out in the 1960s, focusing on the analysis of their concentrations and effects, finding relations for their hardness, sizes, and the impact on bearing life [23]. In reference [24], the authors compared SVM models with a radial basis function kernel fed with statistical features against a CNN structure to diagnose two levels of sand dust contamination on bearings using vibration signals, showing 100% effectiveness in the classification of the healthy, 50, and 100 mg of contamination classes using RMS on the SVM model and 98.33% with the CNN.