Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals

(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine¿s integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of Bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the Bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224) and in part by MEC under Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino-Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA.; Dunai, L. (2021). Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals. IEEE Transactions on Industry Applications. 57(6):5876-5886. https://doi.org/10.1109/TIA.2021.3108413S5876588657

Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes

In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique

Novel Hilbert Huang transform techniques for bearing fault detection

Bearings are commonly used in rotary machinery; while up to half of machinery malfunctions could be related to bearing defects. A reliable bearing fault detection technique becomes vital to a wide array of industries to recognize an incipient bearing defect to prevent machinery performance degradation, malfunction, and unexpected breakdown. Many signal processing techniques have been suggested in literature to extract fault-related signatures for bearing fault detection, but most of them are not robust in real-world bearing health condition monitoring when signal properties vary with time. Vibration signals generated from bearings can be either stationary or nonstationary. If bearing defect-related signature is stationary, it is relatively easy to analyze using these classical data analysis techniques. However, bearing nonstationary signals are much more complex to analyze using these classical signal processing techniques, especially when slippage has occurred. Reliable fault detection still remains a challenging task, especially when bearing defect-related features are nonstationary. Two alternative approaches are proposed in this work for bearing fault detection: The first technique is based on analytical normality test, named Normalized Hilbert Haung Transform (NHHT). The second technique is based on information domain analysis, named enhanced Hilbert Haung Transform (eHHT). In the proposed NHHT technique, a novel strategy based on d’Agostino-Pearson normality analysis is suggested to demodulate feature functions and highlight feature characteristics for bearing fault detection. In the proposed eHHT, a novel strategy is proposed to enhance feature extraction based on the analysis of correlation and mutual information. The effectiveness of the proposed techniques is verified by a series of experimental tests corresponding to different bearing health conditions. Their robustness in bearing fault diagnostic is examined by the use of data sets from a different experimental setup

Fault detection and diagnosis of a multistage helical gearbox using magnitude and phase information from vibration signals

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Bearing health assessment based on chaotic characteristics

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Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

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THE ANALYSIS OF POWER SUPPLY SIGNALS BY INCLUDING PHASE EFFECTS FOR MACHINE FAULT DIAGNOSIS

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