Surge detection methods using empirical mode decomposition and continuous wavelet transform for a centrifugal compressor

Abstract. This study examines the utility of the Empirical Mode Decomposition (EMD) time-series analysis technique to separate the horizontal wind field observed by the Scott Base MF radar (78° S, 167° E) into its constituent parts made up of the mean wind, gravity waves, tides, planetary waves and instrumental noise. Analysis suggests that EMD effectively separates the wind field into a set of Intrinsic Mode Functions (IMFs) which can be related to atmospheric waves with different temporal scales. The Intrinsic Mode Functions resultant from application of the EMD technique to Monte-Carlo simulations of white- and red-noise processes are compared to those obtained from the measurements and are shown to be significantly different statistically. Thus, application of the EMD technique to the MF radar horizontal wind data can be used to prove that this data contains information on internal gravity waves, tides and planetary wave motions. Examination also suggests that the EMD technique has the ability to highlight amplitude and frequency modulations in these signals. Closer examination of one of these regions of amplitude modulation associated with dominant periods close to 12 h is suggested to be related to a wave-wave interaction between the semi-diurnal tide and a planetary wave. Application of the Hilbert transform to the IMFs forms a Hilbert-Huang spectrum which provides a way of viewing the data in a similar manner to the analysis from a continuous wavelet transform. However, the fact that the basis function of EMD is data-driven and does not need to be selected a priori is a major advantage. In addition, the skeleton diagrams, produced from the results of the Hilbert-Huang spectrum, provide a method of presentation which allows quantitative information on the instantaneous period and amplitude squared to be displayed as a function of time. Thus, it provides a novel way to view frequency and amplitude-modulated wave phenomena and potentially non-linear interactions. It also has the significant advantage that the results obtained are more quantitative than those resultant from the continuous wavelet transform.

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IDENTIFICATION OF ROTATING PRESSURE WAVES IN A CENTRIFUGAL COMPRESSOR DIFFUSER BY MEANS OF THE WAVELET CROSS-CORRELATION

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691306001312 ◽

2006 ◽

Vol 04 (02) ◽

pp. 373-382 ◽

Cited By ~ 8

Author(s):

LONGIN HORODKO

Keyword(s):

Correlation Function ◽

Wavelet Transform ◽

Centrifugal Compressor ◽

Continuous Wavelet Transform ◽

Signal Analysis ◽

Cross Correlation ◽

Rotating Stall ◽

Pressure Waves ◽

Continuous Wavelet ◽

Compressor Impeller

Rotating stall is usually the first symptom of approaching compressor instability. It consists of zones of a turbulent flow, which rotate slower than the compressor impeller. The wavelet cross-correlation function was applied to detect rotating objects, whereas the continuous wavelet transform was used to identify different phases of the compressor operation. These methods of signal analysis enabled the identification of rotating pressure waves that appear during all phases of operation. Some of them have several times shorter wavelength than these manifested by the rotating stall. What is more, rotating pressure waves, though very weak, appear also during the stable compressor operation.

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Damage Localization of Piles Based on Complex Continuous Wavelet Transform: Numerical Example and Experimental Verification

Shock and Vibration ◽

10.1155/2020/8058640 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Wenting Zheng ◽

Sifan Wang ◽

Chengxu Lin ◽

Xianying Yu ◽

Jingliang Liu

Keyword(s):

Wavelet Transform ◽

Experimental Verification ◽

Continuous Wavelet Transform ◽

Wavelet Coefficient ◽

Parameter Analysis ◽

Detection Methods ◽

Damage Localization ◽

Continuous Wavelet ◽

Numerical Example ◽

Phase Angles

A new signal processing method called complex continuous wavelet transform (CCWT) is introduced in this paper to localize pile damage because it clearly reveals inherent characteristics of response signals. In this method, CCWT is first performed on the response signal to obtain the wavelet coefficient matrix. The resultant coefficients are then employed to calculate phase angles at different frequency bands with an aim of pile damage localization. However, the CCWT method is only demonstrated via laboratory tests on pile specimens, and its application on actual piles has not been examined. Moreover, various factors such as pile-soil interaction need to be considered when the CCWT method is applied on actual piles. To address these issues, a numerical example of 3D finite element pile model followed by a parameter analysis and an experimental verification on an actual pile are investigated. The results demonstrate that the CCWT method is capable of localizing pile damage under different damage scenarios. However, there are still some interference points in the grayscale images of phase angles and the reduction of interference points needs to be addressed by mutual verification with other pile damage detection methods and engineering experience.

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Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform

Geophysics ◽

10.1190/geo2015-0598.1 ◽

2016 ◽

Vol 81 (4) ◽

pp. V341-V355 ◽

Cited By ~ 95

Author(s):

S. Mostafa Mousavi ◽

Charles A. Langston ◽

Stephen P. Horton

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Single Channel ◽

Simultaneous Detection ◽

Onset Time ◽

Detection Methods ◽

Continuous Wavelet ◽

Local Maxima ◽

Synthetic Signal ◽

Microseismic Data

Typical microseismic data recorded by surface arrays are characterized by low signal-to-noise ratios (S/Ns) and highly nonstationary noise that make it difficult to detect small events. Currently, array or crosscorrelation-based approaches are used to enhance the S/N prior to processing. We have developed an alternative approach for S/N improvement and simultaneous detection of microseismic events. The proposed method is based on the synchrosqueezed continuous wavelet transform (SS-CWT) and custom thresholding of single-channel data. The SS-CWT allows for the adaptive filtering of time- and frequency-varying noise as well as offering an improvement in resolution over the conventional wavelet transform. Simultaneously, the algorithm incorporates a detection procedure that uses the thresholded wavelet coefficients and detects an arrival as a local maxima in a characteristic function. The algorithm was tested using a synthetic signal and field microseismic data, and our results have been compared with conventional denoising and detection methods. This technique can remove a large part of the noise from small-amplitudes signal and detect events as well as estimate onset time.

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