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ABSTRACT: In this paper, a new induction motor diagnosis methodology is proposed. The approach is based on obtaining a 2-D time-frequency plot representing the time-frequency evolution of the main components in an electrical machine transient current. The identification of characteristic patterns in the time-frequency plane caused by many of the fault-related components enables a reliable machine diagnosis. Unlike other continuous-wavelet-transform-based methods, this work uses frequency B-spline (FBS) wavelets. It is shown that these wavelets enable an efficient filtering in the region neighboring the main frequency, as well as enable a high level of detail in the time-frequency maps. As a consequence, the evolution of the most important current components is precisely traced. These characteristics make it easy to identify the patterns related to the fault components. The technique is applied to the experimental no-load start-up current of motors in a healthy state and with broken bars; the FBS capabilities are revealed. One of the novelties of this paper is the fact that the diagnosis is carried out via the identification not only of the traditional lower sideband harmonic but also of the upper sideband harmonic and four additional fault-related components.
IEEE Transactions on Industrial Electronics 06/2011; · 5.16 Impact Factor
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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: The analysis of the stator startup current for the diagnosis of mixed eccentricities has been revealed as an alternative reliable way for detecting the presence of this fault. It avoids some of the constraints of the classical diagnosis approach, based on the identification of the eccentricity-related components appearing in the FFT spectrum of the steady-state current. In this context, the selection of suitable Time-Frequency Decomposition (TFD) tools for the analysis of non-stationary quantities-such as the startup current- plays a crucial role. Previous works have proven the reliability of the discrete wavelet transform (DWT) and Hilbert-Huang transform (HHT) for tracing the transient evolution of eccentricity-related components and, therefore, for the diagnosis of the fault. In this paper, a different tool, the Wigner Ville distribution (WVD) is applied to the diagnosis of mixed eccentricities, making special emphasis in the detection of high frequency components introduced by this fault. Moreover, a brief comparison with respect to HHT and DWT is carried out, showing the main advantages and drawbacks of each tool regarding the transient-based diagnosis of the aforementioned fault.
Industrial Electronics, 2009. IECON '09. 35th Annual Conference of IEEE; 12/2009
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ABSTRACT: In this paper a new methodology of transient motor current signature analysis (TMCSA) is proposed. The approach consists on obtaining a 2D time frequency plot representing the time-frequency evolution of all the harmonics present on an electric machine transient current. Identifying characteristic patterns in the time-frequency plane, produced by some of the fault related components, permits the machine diagnosis. Unlike other CWT based methods, this work uses complex frequency B-splines wavelets. It is shown that these wavelets enable high detail in the time-frequency maps and an efficient filtering in the region neighbouring the main frequency. These characteristics make easy the identification of the patterns related to the fault components. As an example, the technique has been applied to no load startup currents of healthy motors and motors with broken bars, showing the Complex FBS Wavelets capabilities. The diagnosis has been done via the identification of the upper sideband harmonic.
Diagnostics for Electric Machines, Power Electronics and Drives, 2009. SDEMPED 2009. IEEE International Symposium on; 10/2009
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ABSTRACT: The Hilbert Transform (HT) can improve the resolution of motor current signature analysis (MCSA), especially at very low slip, because it converts the supply frequency into a continuous component, which can be easily removed to better detect fault harmonics. This paper proposes its application also during speed transients, with two key advantages: first, it allows an easy filtering of the transient current component corresponding to the supply frequency, and, second, the HT allows for the generation of the Hilbert Spectrum, as a replacement of the Fourier Spectrum in the case of non-stationary signals, like those that appear in a transient regime. The performance of the proposed method is compared with other methods as the Discrete Wavelet Transform (DWT), and is validated through simulation with a mathematical model and experimental analysis of a 1.1 kW three-phase squirrel-cage commercial induction motor with eccentricity.
Diagnostics for Electric Machines, Power Electronics and Drives, 2009. SDEMPED 2009. IEEE International Symposium on; 10/2009
