DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors
ABSTRACT 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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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.Mechanical Systems and Signal Processing 10/2014; · 1.91 Impact Factor
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ABSTRACT: This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of Complex Empirical Mode Decomposition to the measured start-up current and on the subsequent extraction of a specific complex Intrinsic Mode Function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on Hidden Markov Models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology.IEEE Transactions on Industrial Electronics 09/2014; 61(9):4937-4946. · 6.50 Impact Factor
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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.Annual Conference of the IEEE Industrial Electronics Society (IECON), Vienna; 11/2013