Abstract
Abstract This work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.
Highlights
The monitoring and diagnosis of rotary electric machines have taken great importance in current applications of industry, aeronautics, and telecommunications, among other branches of science and technology
Iglesias-Martínez et al Applied Mathematics and Nonlinear Sciences 5(2020) 1–14 machines. These techniques have been designed to increase machines efficiency, safety, and performance, from the reliability and energy point of view [17, 32]. Each one of these techniques are based on processing different signals that can be captured by sensors that measure parameters such as mechanical vibrations, stator current, acoustic signal, and stray flux
We focus on providing an update of the statistical analysis techniques based on cumulants and higher-order spectra applied to the diagnosis of failures using stray flux signals
Summary
The monitoring and diagnosis of rotary electric machines have taken great importance in current applications of industry, aeronautics, and telecommunications, among other branches of science and technology. Each one of these techniques are based on processing different signals that can be captured by sensors that measure parameters such as mechanical vibrations, stator current, acoustic signal, and stray flux They let us monitor the condition of electric machines and detect numerous failures such as design and manufacture defects, improper ambient conditions, overload and over-speed, fatigue, stator insulation failure, bearing fault, broken rotor bar/end-ring detection, and air gap eccentricity [17]: One of the most commonly used techniques is vibration analysis [10, 12, 58] and motor-current analysis or motor current signature analysis (MCSA) [35, 64, 65]. The treatment of failures in electric induction machines has taken a high boom in relation to the analysis of the stray flux signals for the detection of failures, see the recent review of Jiang et al [32] It is not considered based on the use of statistical analysis of cumulants nor High-Order Spectra Analysis (HOSA). We provide some conclusions and prospective lines for future work in this line
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