Abstract
Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.
Highlights
A great deal of the applications in the modern industry is composed of electromechanical systems or kinematic chains that generally involve electrical rotating machinery
It has been reported by the International Energy Agency (IEA) that electric motors demand more than half of the global electricity [1,2]; it has been estimated that such electrical demand may grow approximately 2.1% per year
Aiming to face these issues, the development of condition monitoring strategies focused on fault detection and identification in Induction Motor (IM), and its linked components, is being one of the priorities addressed by new research works
Summary
A great deal of the applications in the modern industry is composed of electromechanical systems or kinematic chains that generally involve electrical rotating machinery. Even though good performance results are obtained, the use of ensemble learning-based on DL results in a complicated task; these challenges can be overcome by the proposal of strategies that incorporate proper processing techniques, i.e., analysis in the time-domain and frequency-domain, and by considering multiple signals that contain different information, but its fusion leads to highlighting those representative fault-related patterns. Most of the proposed studies that have analyzed and identified the occurrence of faults in bearings are commonly focused on analyzing those faults that are related to the electrical erosion phenomenon [13,15,28] In this regard, in the research field, the faults produced by electrical erosion are artificially produced by drilling a hole in the outer race, inner race and/or by damaging the rolling elements of the bearing [29,30,31]. It should be noted that these fault-related frequency components are computed in terms of the number of rolling elements (balls), Nb; the ball diameter, BD; the pitch diameter, PD; the contact angle, θ; and the rotational frequency, frm, [41]
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