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ABSTRACT: In this paper a recently developed induction motors diagnosis methodology is applied to detect mixed eccentricity in Inverter-Fed Induction Motors (IFIMs). The classic FFT method can not be applied when the stator current captured is not in steady state (which is common in IFIMs). The approach is based on obtaining a 2D time - frequency plot representing the time - frequency evolution of the main components in a stator transient current. The time-frequency maps are generated with high detail using the Analytic Wavelet Transform. Thanks to this, the evolutions of the main Winding Harmonics, Principal Slot Harmonics and Eccentricity Related Harmonics are traced precisely. As a consequence, the time-frequency plane characteristic patterns produced by the Eccentricity Related Harmonics are easily and clearly identified enabling a reliable diagnosis. The methodology capabilities have been shown successfully diagnosing a healthy IFIM and an IFIM with mixed eccentricity. The transients analyzed consist of a startup and a decrease in the assigned frequency.
Electrical Machines (ICEM), 2010 XIX International Conference on; 10/2010
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ABSTRACT: In this paper, the tracing of the right sideband component (RSC) evolution in the stator startup current is proposed for the diagnosis of rotor asymmetries in induction machines. Although several works have dealt with the detection of the left sideband component (LSC) during the transient, few contributions have focused on the RSC, perhaps due to its more difficult detection, since it has often lower amplitude. In this work, several signal processing techniques, such as the short time Fourier transform (STFT), the discrete wavelet transform (DWT), the continuous wavelet transform (CWT) and band pass filtering are applied in order to extract this component during the transient. Several experimental startup current signals are used for this purpose. The results show that the transient extraction of the RSC might constitute an additional source of information which could enable a more reliable diagnosis of the fault, mainly in those cases in which the transient LSC evolution could be partially masked by other phenomena.
Electrical Machines, 2008. ICEM 2008. 18th International Conference on; 10/2008
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ABSTRACT: The identification and extraction of characteristic patterns are proposed in this work for the diagnosis and evaluation of mixed eccentricities in induction electrical machines with parallel stator branches. Whereas the classical diagnosis approaches, deeply spread in the industrial environment, are based on the Fourier analysis of the steady-state current, the basis of the proposed methodology consist of analysing the current demanded by the machine during the connection process (startup transient); the objective is to extract the characteristic evolution during the transient of some harmonic components created by the fault; this evolution is caused by the dependence of these components on the slip (s), a quantity varying during the startup transient from 1 to almost 0. For this feature extraction, the Hilbert–Huang Transform (HHT) is proposed. An analysis of the behaviour of this transform in comparison with another time-frequency approach used in other works, the Discrete Wavelet Transform (DWT), is also presented in the paper. The results show the usefulness of the methodology for the reliable diagnosis of the mixed eccentricity fault and for the correct discrimination against other types of failures.
Energy Conversion and Management.
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ABSTRACT: The main objective of this paper is to diagnose the presence of combined faults in induction machines. For this purpose, a methodology based on the application of the Discrete Wavelet Transform (DWT) to the stator startup current is used. This approach was applied in previous works with success to the diagnosis of rotor asymmetries and mixed eccentricities in motors with different sizes and conditions. However, as most of the diagnosis methods hitherto developed, the application of the proposed approach was circumscribed to situations in which a single fault was present in the machine. In addition, the influence of other phenomena such as load torque oscillations or voltage fluctuations was studied, but without considering the combination of these phenomena and the fault in the machine. This work is intended, first, to apply the proposed transient-based methodology to several cases in which different faults (rotor asymmetries, mixed eccentricities and inter-turn and inter-coil stator short-circuits) are simultaneously present in the machine and, second, to apply it to cases regarding faults combined with other phenomena making difficult the diagnosis, such as load torque oscillations. Interesting considerations regarding the preponderance of the effects of some of the faults are also done in the paper. The application of the methodology is focused on induction machines with stator parallel branches; in this sense, the suitability of the use either of the phase current or of the branch current for the diagnosis of each particular fault is analysed. The results look promising with regard to the validity of the methodology for the reliable discrimination of simultaneous electromechanical faults and the diagnosis of faults combined with other phenomena.
Mechanical Systems and Signal Processing.
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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.
Mechanical Systems and Signal Processing.
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ABSTRACT: The aim of this paper is to present and validate a methodology for diagnosing rotor asymmetries in cage motors, based on the analysis of the stator startup current. The method consists of the extraction of a harmonic component introduced by this fault – the left sideband component – from the stator startup current. Two alternative techniques developed by different research groups are proposed for the transient extraction of this component; the digital low-pass filtering (DLPF) and the discrete wavelet transform (DWT). Both approaches are applied to three different industrial motors ranging from 1.1 to 450 kW. A detailed explanation of the physical basis of the method and comments related to the application scope of the approach are also given. The results show the robustness of both approaches for the reliable diagnosis of the fault and suggest a clear potentiality for extending the methodology to the detection of other types of faults introducing components dependant on the slip.
Electric Power Systems Research.
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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 21(2):268-279. · 3.45 Impact Factor
