## 6 Conclusions  
Most industrial machines are based on electromechanical systems or kinematic chains that are mainly driven by electrical rotating machines, such as IMs, where bearings are the elements that allow its rotating movement, and the sudden appearance of faults is inherent to its operation. In this regard, in this work, a condition monitoring methodology designed for identifying different severities of the outer race bearing fault in an IM is proposed. The proposed method is supported by tools, such as Machine Learning (ML) techniques and Artificial Intelligence (AI), that facilitates the fusion information of different physical magnitudes and that leads to high-performance pattern characterization. Specifically, this work proposes the fusion information of different physical magnitudes, such as vibrations and stator current, which are characterized through a hybrid set of statistical features that is estimated by the signal processing in two different domains: the time-domain and frequency-domain. This proposal aims to promote the detection of different fault severities in the outer race of a bearing can be effectively performed by the use of multiple physical magnitudes, such as vibrations and stator current, that are processed in multiple domains, leading to high-performance signal characterization. In fact, the detection and evolution assessment of incipient faults that appear in bearings can be a complex task since the fault-related patterns may not show significant changes compared to when several damages are induced; that is, the identification of fault graduality may be difficult whether physical magnitudes, such as stator current, show small changes and/or variations in amplitude. Thus, the main contributions of this work include the use of different physical magnitudes for estimating the characteristic patterns associated to the bearing condition; indeed, the estimation of statistical features through different domain analyses results in high-performance signal characterization. Moreover, the use of ML techniques, such as the LDA, allows performing the fusion of information because the compression and reduction of information is carried out by mapping, with different weights, the original feature space into a new space of lower dimension (i.e., 2D). Consequently, the characterization of vibration and stator current signal through statistical features from different domains, together with its fusion through the LDA technique, facilitates the classification task performed by the NN-based classifier. In this regard, it is proved that, by means of the proposed method, a global classification ratio higher than 98.7% is obtained during  
Figure 8: The resulting decision regions achieved during the training of the NN-based classifier for detecting the considered bearing fault severities. the diagnosis of different fault severities that may appear in the bearing outer race. On the other side, the proposed approach presents a coherent design of the structure that includes Machine Learning and Artificial Intelligence that, in comparison with other related works that propose ensemble learning structures based on Deep Learning, allows correctly identifying different severities in the outer race of bearings. Moreover, it should be highlighted that this proposed methodology has been designed to overcome and face the occurrence of unexpected faults that may appear insignificant but may gradually increase until producing irreversible damages. The proposed method is validated under a complete set of experimental data that considered a healthy condition (HC) and five severities in the outer race of a bearing (S1, S2, S3, S4, and S5). For future work, the analysis of the remaining useful life remains a research topic that must be addressed, and the analysis of combined faults that may occur simultaneously in different parts of bearings also needs to be addressed.