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On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis
Medoued, Ammar,Lebaroud, Abdesselem,Boukadoum, Ahcene,Clerc, Guy The Korean Institute of Electrical Engineers 2010 Journal of Electrical Engineering & Technology Vol.5 No.1
The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.
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A Weighted Bio-signal Denoising Approach Using Empirical Mode Decomposition
Salim Lahmiri,Mounir Boukadoum 대한의용생체공학회 2015 Biomedical Engineering Letters (BMEL) Vol.5 No.2
Purpose The purpose of this study is to show the effectiveness of a physiological signal denoising approach called EMDDWT- CLS. Methods This paper presents a new approach for signal denoising based on empirical mode decomposition (EMD), discrete wavelet transform (DWT) thresholding, and constrained least squares (CLS). In particular, the noisy signal is decomposed by empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) plus a residue. Then, each IMF is denoised by using the discrete wavelet transform (DWT) thresholding technique. Finally, the denoised signal is recovered by performing a weighted summation of the denoised IMFs except the residue. The weights are determined by estimating a constrained least squares coefficients; where, the sum of the coefficients is constrained to unity. We used human ECG and EEG signals, and also two EEG signals from left and right cortex of two healthy adult rats. Results The 36 experimental results show that the proposed EMD-DWT-CLS provides higher signal-to-noise ratio (SNR) and lower mean of squared errors (MSE) than the classical EMD-DWT model. Conclusions Based on comparison with classical EMDDWT model used in the literature, the proposed approach was found to be effective in human and animal physiological signals denoising.
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On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis
Ammar Medoued,Abdesselem Lebaroud,Ahcene Boukadoum,Guy Clerc 대한전기학회 2010 Journal of Electrical Engineering & Technology Vol.5 No.1
The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.
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