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Spectral density estimation

About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors proposed a sliding window discrete Fourier transform and the effect of sidelobes of sideband frequencies on the fundamental component amplitude of stator current, which can detect the amplitude of the fault indicator frequency in vicinity of the fundamental one.
Abstract: The Fourier transform is widely used to diagnose induction motor faults through the monitoring of fault signatures from measured signals such as stator currents. For a good frequency resolution, Fourier transform needs a long signal acquisition time that increases the probability of speed fluctuations which, leads to fault signatures variations. In addition, limited acquisition time and acquired points generate unwanted sidelobes leakage phenomenon, caused by step frequency resolution. In signal processing, the use of window functions allows the avoidance of this phenomenon with the cost of losing a part of signal information. In this paper, the authors propose a new method for the diagnosis of induction motor broken bar fault based on sliding window discrete Fourier transform and the effect of sidelobes of sideband frequencies on the fundamental component amplitude of stator current. The main advantage of the proposed method is that one can detect the amplitude of the fault indicator frequency in vicinity of the fundamental one in shorter time and with good precision even if the motor turns at no-load when compared to used methods, as fast Fourier transform, zoom fast Fourier transform, multiple signal classification, and zoom multiple signal classification. The simulation and experimental results validate the effectiveness of the proposed method.

49 citations

Proceedings ArticleDOI
05 Jun 2000
TL;DR: Novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence in nonlinear non-Gaussian dynamical models.
Abstract: We develop methods for performing filtering and smoothing in nonlinear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.

49 citations

Proceedings ArticleDOI
Jan Verspecht1
18 May 1993
TL;DR: A method is described which allows an accurate estimation of the values of the spectral components of a signal, with significant timebase error present in a digitized sine wave.
Abstract: The timebase distortion present in an equivalent-time sampling oscilloscope introduces errors in the estimation of the values of the spectral components of a microwave signal when a classical discrete Fourier transform is used. A method is developed here to avoid these errors. The method is tested both in practice and with simulations. Two parts can be distinguished. At first, the timebase distortion is measured. This is done by digitizing a sinusoidal signal applied at the oscilloscope's input, and by calculating the phase of the analytical signal of the digitized waveform. Other possible methods to measure the timebase distortion are discussed, and it is shown why the method used is the most appropriate for our specific application. The knowledge of the timebase distortion is then used to build a least squares error estimator for the values of the spectral components of a digitized microwave signal. An experimental verification is done, from which is concluded that the method effectively removes the spectral estimation errors due to a timebase distortion. >

49 citations

Journal ArticleDOI
TL;DR: A recursive algorithm to implement phase retrieval from two intensities in the fractional Fourier transform domain is proposed that can significantly simplify computational manipulations and does not need an initial phase estimate compared with conventional iterative algorithms.
Abstract: We first discuss the discrete fractional Fourier transform and present some essential properties. We then propose a recursive algorithm to implement phase retrieval from two intensities in the fractional Fourier transform domain. This approach can significantly simplify computational manipulations and does not need an initial phase estimate compared with conventional iterative algorithms. Simulation results show that this approach can successfully recover the phase from two intensities.

48 citations

Journal ArticleDOI
TL;DR: The findings suggest that the time-frequency analysis provides instantaneous metrics which describe the amplitude changes and frequency shift of the center of pressure under a variety of environmental conditions, thus providing a more reliable quantification of postural control.

48 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
202248
202159
2020101
201994
201895