DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors

Department of Electrical Engineering, Polytechnic University of Valencia, P.O. Box 22012, 46071 Valencia, Spain
Mechanical Systems and Signal Processing (Impact Factor: 2.26). 08/2007; 21(6):2575-2589. DOI: 10.1016/j.ymssp.2007.01.008


The behaviour of an induction machine during a startup transient can provide useful information for the diagnosis of electromechanical faults. During this process, the machine works under high stresses and the effects of the faults may also be larger than those in steady-state. These facts may help to amplify the magnitude of the indicators of some incipient faults. In addition, fault components with frequencies dependant on the slip evolve in a particular way during that transient, a fact that allows the diagnosis of the corresponding fault and the discrimination between different faults. The discrete wavelet transform (DWT) is an ideal tool for analysing signals with frequency spectrum variable in time. Some research works have applied with success the DWT to the stator startup current in order to diagnose the presence of broken rotor bars in induction machines. However, few works have used this technique for the study of other common faults, such as eccentricities. In this work, time–frequency analysis of the stator startup current is carried out in order to detect the presence of dynamic eccentricities in an induction motor. For this purpose, the DWT is applied and wavelet signals at different levels are studied. Data are obtained from simulations, using a finite element (FE) model of an induction motor, which allows forcing several kinds of faults in the machine, and also from experimental tests. The results show the validity of the approach for detecting the fault and discriminating with respect to other failures, presenting for certain applications (or working conditions) some advantages over the traditional stationary analysis.

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    • "Among the varieties of wavelet analysis, CWT [21] [22] and WPT [23] [24] have not been widely applied in the field of motor fault diagnosis compared to the widely used DWT based techniques [25] [26] [27] [28] [29]. However, the effectiveness of DWT is weakened by a few inevitable limitations [30]. "
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    ABSTRACT: Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform such that fault feature adaptability is enabled. Within ESW, a parametric optimization is performed on the measured signal to obtain a quality TQWT basis that best demonstrate the hidden fault feature. TQWT is introduced as it provides a vast wavelet dictionary with time-frequency localization ability. The parametric optimization is guided according to the maximization of fault feature ratio, which is a new quantitative measure of periodic fault signatures. The fault feature ratio is derived from the digital Hilbert demodulation analysis with an insightful quantitative interpretation. The output of ESW on the measured signal is a selected wavelet scale with indicated fault features. It is verified via numerical simulations that ESW can match the oscillatory behavior of signals without artificially specified. The proposed method is applied to two engineering cases, signals of which were collected from wind turbine and steel temper mill, to verify its effectiveness. The processed results demonstrate that the proposed method is more effective in extracting weak fault features of induction motor bearings compared with Fourier transform, direct Hilbert envelope spectrum, different wavelet transforms and spectral kurtosis.
    Full-text · Article · Oct 2014 · Mechanical Systems and Signal Processing
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    • "In this context, in the early 2000's a new diagnosis approach was proposed for fault diagnostics in squirrel-cage induction motors [6] [7] [8] [15] [16]. Unlike the traditional FFTbased technique, relying on the analysis of stationary quantities (mainly steady-state currents), the new method proposes the study and the analysis of quantities (mainly currents) during transient operation. "
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    ABSTRACT: Most of the research work hitherto carried out in the induction motors fault diagnosis area has been focused on squirrel-cage motors in spite of the fact that wound-rotor motors are typically less robust, having a more delicate maintenance. Over recent years, wound-rotor machines have drawn an increasing attention in the fault diagnosis community due to the advent of wind power technologies for electricity generation and the widely spread use of its generator variant, the Doubly-Fed Induction Generators (DFIGs) in that specific context. Nonetheless, there is still a lack of reliable techniques suited and properly validated in wound-rotor industrial induction motors. This paper proposes an integral methodology to diagnose rotor asymmetries in wound-rotor motors with high reliability. It is based on a twofold approach; the Empirical Mode Decomposition (EMD) method is employed to track the low frequency fault-related components, while the Wigner-Ville Distribution (WVD) is used for detecting the high-frequency failure harmonics during a startup. Experimental results with real wound-rotor motors demonstrate that the combination of both perspectives enables to correctly diagnose the failure with higher reliability than alternative techniques relying on a unique informational source.
    Full-text · Conference Paper · Nov 2013
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    • "Due to perceptible effect of the noise on this index, using magnitude of the side band components at frequencies around the fundamental harmonic and PSH has been recommended in [6]. Some recent studies have applied wavelet transform to the stator current for fault detection, in the steadystate or the startup of the motor [11] [12] and the extracted features usually are energy and peak of decomposed signals at various levels of wavelet decomposition. In [13], continuous wavelet transform of stator current is used as a method for feature extraction, where the amplitude of local minima in various scales of continuous wavelet transform of stator current with Morlet basis function is used as indices for eccentricity detection. "
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    ABSTRACT: Simultaneous static eccentricity and broken rotor bars faults, called mixed-fault, in a three-phase squirrel-cage induction motor is analyzed by time stepping finite element method using fast Fourier transform. Generally, there is an inherent static eccentricity (below 10%) in a broken rotor bar induction motor and therefore study of the mixed-fault case could be considered as a real case. Stator current frequency spectrum over low frequencies, medium frequencies and high frequencies are analyzed; static eccentricity diagnosis and its distinguishing from the rotor bars breakage in the mixed-fault case are described. The contribution of the static eccentricity and broken rotor bars faults are precisely determined. Influence of the broken bars location upon the amplitudes of the harmonics due to the mixed-fault is also investigated. It is shown that the amplitudes of harmonics due to broken bars placed on one pole are larger than the case in which the broken bars are distributed on different poles. In addition, influence of varying load on the amplitudes of the harmonics due to the mixed-fault is studied and indicated that the higher load increases the harmonics components amplitudes due to the broken bars while the static eccentricity degree decreases. Simulation results are confirmed by the experimental results.
    Full-text · Article · Jul 2010 · Energy Conversion and Management
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