A Comparative Study of High-Accuracy Frequency Estimation Methods

Santamarı́a, Ignacio; Pantaleón, C.; Ibáñez, J.

doi:10.1006/mssp.2000.1321

Cited by 56 publications

(31 citation statements)

References 15 publications

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“…However, they share several drawbacks, namely aliasing, resolution bias error or picket-fence eect and leakage [23]. On the other hand, with a slight increase in the computational cost, a parametric method is able to obtain high-accuracy frequency estimates working with a relatively short data set [24]. Among these parametric methods the Prony's method is a good compromise between accuracy and computational eort.…”

Section: Is a Stationary Process (Where D Is A Nonnegativementioning

confidence: 99%

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

David¹,

Sánchez-Urán²,

Álvarez³

et al. 2013

Electric Power Systems Research

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This paper proposes a method for the identication of dierent partial discharges (PD) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm.Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure.The result of the unsupervised classication is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classication results permits both the identication of dierent PD sources and the discrimination between original PD signals, reections, noise and external interferences. The squared gain function associated with the wavelet lter at scale j.MRA Multiresolution analysis.

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Section: Is a Stationary Process (Where D Is A Nonnegativementioning

confidence: 99%

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

David¹,

Sánchez-Urán²,

Álvarez³

et al. 2013

Electric Power Systems Research

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show abstract

“…This is an algorithm that is applicable for adding any symmetrical window function, and notably has noise immunity. 15 Despite the above features, different disadvantages of the above algorithms cannot be avoided, especially when random noise is present in the signal spectrum of mechanical failure, resulting in the low accuracy of their estimates. [16][17][18] The noise will also seriously affect the accuracy of the spectrum estimation.…”

Section: Introductionmentioning

confidence: 99%

Fault pattern recognition of gas blower based on discrete Fourier transform interpolation algorithm

Xie

Chen

2017

Advances in Mechanical Engineering

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The mechanical equipment fault spectrum often contains random noise, and traditional spectrum estimation methods introduce large errors in the fault diagnosis of mechanical equipment including gas blower. Accurate frequency estimation of multi-frequency signals that are contaminated is a common problem. To address the fault spectral characteristics of gas blowers, a novel discrete Fourier transform interpolation algorithm is presented based on traditional practice. The algorithm combines the zero-complement technique and the main lobe fitting technique. The accuracy of the spectrum correction was improved greatly with the new algorithm, and it could effectively promote fault pattern recognition. Application examples and simulation results showed that the new algorithm is very successful in the fault pattern recognition of gas blowers. Under noise conditions, the proposed algorithm has a smaller estimation error than conventional algorithms and could achieve high precision, strong compatibility, and robustness. Theoretically, the algorithm has attained a higher level, and its application in engineering practice is also quite impressive due to its highly expected values.

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“…A simplified linear model is adopted due to the complexity of the comprehensive complex-valued model. Based on the quantitative relationship between the phases resulting from the DFTs of the original signal and the shifted signal with the same or different length, the phase-difference-based method is initially proposed only for estimating the frequency of a monochromatic signal [24][25][26], and is fully developed afterwards [5,27]; it requires calculation of DFT twice. From different points of view, similar ideas are explored by Danthez [3] and Dai [28].…”

Section: Introductionmentioning

confidence: 99%

Noise Influence on Estimation of Signal Parameter From the Phase Difference of Discrete Fourier Transforms

Zhu

Li²,

Ding

et al. 2002

Mechanical Systems and Signal Processing

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A Comparative Study of High-Accuracy Frequency Estimation Methods

Cited by 56 publications

References 15 publications

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

Fault pattern recognition of gas blower based on discrete Fourier transform interpolation algorithm

Noise Influence on Estimation of Signal Parameter From the Phase Difference of Discrete Fourier Transforms

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