This paper proposes an online/offline induction motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms, based on the Hilbert transform. MCSA is a method for motor diagnosis with stator-current signals. Although it is one of the most powerful online methods for diagnosing motor faults, it has some drawbacks that can degrade the performance and accuracy of a motor-diagnosis system. In particular, it is very difficult to detect broken rotor bars when the motor is operating at low slip or under no load, due to fast Fourier transform (FFT) frequency leakage and the small amplitude of the current components related to the fault. Therefore, advanced signal-and-data-processing algorithms are proposed. They consist of a proper sample selection algorithm, a Hilbert transformation of the stator-sampled current, and spectral analysis via FFT of the modulus of the resultant time-dependent vector modulus for achieving MCSA efficiently. Experimental results obtained on a 1.1 kW three-phase squirrel-cage induction motor are discussed.
"Nevertheless, the periodogram and its extensions suffer from a low frequency resolution, which is defined as the ability to distinguish two closely spaced frequency components. In , demodulation technique based on Hilbert transform was used to improve the frequency resolution of MCSA method for rotor asymmetries detection without concern about the signal nature (multi-component signal). If an a priori signal model is assumed, parametric methods can be employed to improve the frequency resolution. "
[Show abstract][Hide abstract] ABSTRACT: Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types.
Mechanical Systems and Signal Processing 02/2015; 52(1). DOI:10.1016/j.ymssp.2014.06.015 · 2.26 Impact Factor
"The demodulation techniques can be classified into monodimensional and multi-dimensional techniques. The monodimensional techniques include the synchronous demodulator , , the Hilbert transform , , , time-frequency distributions ,  or adaptive tracking of sine wave . In , , the authors have used Hilbert transform and Park transform for dynamic rotor faults (broken or cracked rotor bars and dynamic rotor eccentricity) detection. "
[Show abstract][Hide abstract] ABSTRACT: Several studies have demonstrated that induction machine faults introduce phase and/or amplitude modulation of the stator currents. Hence, demodulation of the stator currents is of high interest for induction machines faults detection and diagnosis. The demodulation techniques can be classified into mono-dimensional and multi-dimensional approaches. The mono-dimensional techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator and other approaches. The multi-dimensional approaches include the Concordia transform and the Principal Component Analysis. Once the demodulation has been performed, demodulated signals are further processed in order to measure failure severity. In this paper, we present a comprehensive comparison of these demodulation techniques for eccentricity and broken rotor bars faults detection.
Green Energy, 2014 International Conference on, Sfax (Tunisia); 03/2014
"Therefore, in our context, the multidimensional demodulation techniques seem to be better suited than the conventional HT. The differences can be explained by the intrinsic limitations of the HT: First, the domain of validity of this transform is restricted by the Bedrosian theorem ; then, the instantaneous amplitude and frequency obtained with HT can present overshoots at both ends . Another advantage of the CT and PCA techniques over HT lies in the computational complexity. "
[Show abstract][Hide abstract] ABSTRACT: This paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to sta-tor-current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring.
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