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: The problem of the bar breakage diagnosis in electrical induction cage machines is a matter of increasing concern nowadays, due to the widely spread use of these machines in the industry. The classical approach, focused on the Fourier analysis of the steady-state current, has some drawbacks that could be avoided if a study of the transient behavior of the machine is performed. The discrete wavelet transform (DWT) is an ideal tool for this purpose, due to its suitability for the analysis of signals whose frequency spectrum is variable in time. The paper shows how the study of the high-level signals resulting from the DWT of the transient starting current of an induction motor allows the detection of a particular characteristic harmonic that occurs when a rotor bar breakage has taken place. This constitutes an alternative approach that avoids some problems that the traditional method implies and that can even lead to a wrong diagnosis of the fault. In the work, the application of the DWT for broken bar detection is optimized, regarding certain parameters of the transform such as type of the mother wavelet, number of decomposition levels, order of the mother wavelet and sampling frequency.Applied and Computational Harmonic Analysis 01/2006; 21(2):268-279. · 2.49 Impact Factor
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ABSTRACT: Detection of cage motor broken rotor bars has long been an important but difficult job in the detection area of motor faults. The characteristic frequency component of faulted rotor (CFCFR) is very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection. A new detection method based on wavelet ridge is presented in this paper. Aiming at the motor's starting period during which the motor accelerates progressively and CFCFR approaches the power frequency gradually in frequency spectrum, the wavelet ridge-based method is adopted to analyze this transient procedure and the CFCFR is extracted. The influence of power frequency can be effectively eliminated, and detection accuracy can be greatly improved by using the approach presented in this paper. Also, this is indeed a novel but excellent approach for the detection domain of cage induction motor broken rotor bars.IEEE Transactions on Energy Conversion 10/2003; · 2.43 Impact Factor
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ABSTRACT: Previous work on condition monitoring of induction machines has focused on steady-state speed operation. Here, a new concept is introduced based on an analysis of transient machine currents. The technique centers around the extraction and removal of the fundamental component of the current and analyzing the residual current using wavelets. Test results of induction machines operating both as a motor and a generator shows the ability of the algorithm to detect broken rotor bars.IEEE Transactions on Energy Conversion 04/2005; · 2.43 Impact Factor