Characterization of turbulence scales in the atmospheric surface layer with the continuous wavelet transform

ABSTRACT This paper focuses on time-space characterization of drought conditions in the São Francisco River catchment, on the basis of wavelet analysis of Standardized Precipitation Index (SPI) time series. In order to improve SPI estimation, the procedures for regional analysis with L-moments were employed for defining statistically homogeneous regions. The continuous wavelet transform was then utilized for extracting time-frequency information from the resulting SPI time series in a multiresolution framework and for investigating possible teleconnections of these signals with those obtained from samples of the large-scale climate indexes ENSO and PDO. The use of regional frequency analysis with L-moments resulted in improvements in the estimation of SPI time series. It was observed that by aggregating regional information more reliable estimates of low frequency rainfall amounts were obtained. The wavelet analysis of climate indexes suggests that the more extreme dry periods in the study area are observed when the cold phase of both ENSO and the PDO coincides. While not constituting a strict cause effect relationship, it was clear that the more extreme droughts are consistently observed in this situation. However, further investigation is necessary for identifying particularities in rainfall patterns that are not associated to large-scale climate anomalies.

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Characterization of the chronobiological signals based on the continuous wavelet transform

Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136) ◽

10.1109/iembs.1997.757026 ◽

2002 ◽

Cited By ~ 1

Author(s):

B.M. Wu ◽

F.H.Y. Chan ◽

F.K. Lam ◽

A.M.S. Poon ◽

P.W.F. Poon ◽

...

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Continuous Wavelet

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Characterization of Subdural Stimulation-Induced Afterdischarge Activity Using the Continuous Wavelet Transform

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2015.2494522 ◽

2016 ◽

Vol 63 (7) ◽

pp. 1440-1446

Author(s):

Amir-Homayoon Najmi ◽

William R. S. Webber ◽

Helen Lesser ◽

Ronald Peter Lesser

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Continuous Wavelet

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Evaluation of the ContinuousWavelet Transform for Detection of Single-Point Rub in Aeroderivative Gas Turbines with Accelerometers

10.20944/preprints201805.0170.v1 ◽

2018 ◽

Author(s):

Alejandro Silva ◽

Alejandro Zarzo ◽

Juan Manuel Munoz-Guijosa ◽

Francesco Miniello

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Gas Turbines ◽

Single Point ◽

Continuous Wavelet ◽

Accelerometer Signal ◽

Signal Features ◽

Acceleration Signals ◽

Rotor Model

A common fault in turbomachinery is rotor--casing rub. Shaft vibration, measured with proximity probes, is the most powerful indicator of rotor-stator rub. However, in machines such as aeroderivative turbines, with increasing industrial relevance in power generation, constructive reasons prevent the use of those sensors, being only acceleration signals at selected casing locations available. This implies several shortcomings in the characterization of the machinery condition, associated with a lower information content about the machine dynamics. In this work we evaluate the performance of the Continuous Wavelet Transform to isolate the accelerometer signal features that characterize rotor-casing rub in an aeroderivative turbine. The evaluation is carried out on a novel rotor model of a rotor flexible casing system. Due to damped transients and other short-lived features that rub induces in the signals, the Continuous Wavelet Transform proves being more effective than both Fourier and Cepstrum Analysis. This creates the chance for enabling early fault diagnosis of rub before it may cause machine shutdown or damage.

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