scholarly journals A Hybrid Approach for Time-Varying Harmonic and Interharmonic Detection Using Synchrosqueezing Wavelet Transform

2021 ◽  
Vol 11 (2) ◽  
pp. 752
Author(s):  
Gary W. Chang ◽  
Yu-Luh Lin ◽  
Yu-Jen Liu ◽  
Gary H. Sun ◽  
Johnson T. Yu
Keyword(s):  
Wavelet Transform ◽  
Frequency Component ◽  
Measured Signal ◽  
Time Varying ◽  
Actual Measurement ◽  
Time Frequency ◽  
Frequency Detection ◽  
Hybrid Detection ◽  
Harmonic Pollution ◽  
Non Linear

With widespread non-linear loads and the increasing penetration of distributed generations in the power system, harmonic pollution has become a great concern. The causes of harmonic pollution not only include the integer harmonics, but also interharmonics, which exacerbate the complexity of harmonic analysis. In addition, the output variability of highly non-linear loads and renewables such as electric arc furnaces and photovoltaic solar or wind generation may lead to weakly time-varying harmonics and interharmonics in both frequency and magnitude. These features present challenges for accurate assessment of associated power-quality (PQ) disturbances. To tackle such increasing time-varying PQ problems, a hybrid detection method using synchrosqueezing wavelet transform (SSWT) is proposed. The proposed method first obtains the proper parameter values for the mother wavelet according to numerical computations. The wavelet transform-based synchrosqueezing and a clustering method are applied to determine each frequency component of the waveform under assessment. The time-domain waveform and the associated magnitude of each frequency component is then reconstructed by the inverse SSWT operation. The novelty of the proposed method is that it can decompose the measured waveform containing both harmonics and interharmonics into intrinsic mode functions without the need for fundamental frequency detection. Compared to other time–frequency analysis methods, SSWT has better anti-noise and higher resolution of time–frequency curves; even the measured signal has close frequency components. Simulation results and actual measurement validations show that the proposed method is effective and relatively accurate in time-varying harmonic and interharmonic detection and is suitable for applications in power networks and microgrids that have high penetration of renewables or non-linear loads causing time-varying voltage or current waveforms.

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

Using a Modified Harmonic Wavelet Transform to Characterize Mechanical Shock

2011 ◽  
Vol 54 (2) ◽  
pp. 85-102
Author(s):  
David Smallwood
Keyword(s):  
Wavelet Transform ◽  
Spectral Density ◽  
Estimation Error ◽  
Cross Product ◽  
Constant Time ◽  
Time Varying ◽  
Mechanical Shock ◽  
Time Frequency ◽  
Harmonic Wavelet

A modified harmonic wavelet transform is used to estimate a time varying spectral density. The resolution of the estimate has an approximate constant time-frequency product. The estimation error is directly related to this time-frequency product. Unwanted cross product terms are effectively minimized. Several examples are given: White random, two sine waves, chirps, impulses, sums of exponentially decaying sinusoids, and a pyroshock. It is also shown how realizations can be generated from the modified harmonic wavelet transform estimate of the time varying spectral density.

A Study on Characteristics of Leak Signals of Pipeline Using Acoustic Emission Technique

2006 ◽  
Vol 110 ◽  
pp. 79-88 ◽  
Author(s):  
Min Rae Lee ◽  
Joon Hyun Lee
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Circular Hole ◽  
Frequency Component ◽  
Acoustic Emission Technique ◽  
Acoustic Parameters ◽  
Arrival Times ◽  
Time Frequency ◽  
Aerodynamic Model ◽  
Simulation Results

This study presents an approach to leak detection of pipeline review in terms of theoretical analysis such as acoustics and hydromechanics that should be accompanied by explanation of leakage. The acoustic emission signals during leak from circular hole of different geometries were studied both analytically and experimentally. The relationships between acoustic parameters and fluid mechanical parameters also were derived analytically. A quadrupole aerodynamic model was applied for the analysis of leak from the circular hole. Computer simulation results demonstrate the effectiveness of the proposed approach. In addition, it was confirmed that the wavelet transform (WT) was an effective tool to determine source location. That is, arrival times of each frequency component needed in the velocity calculation could be determined from the peak of the magnitude of wavelet transform data on the time-frequency plane.

STUDY ON THE CONTRIBUTION OF INTAKE NOISE USING COMPLEX TIME-TIME ANALYSIS AND SUBJECTIVE EVALUATION

2010 ◽  
Vol 08 (04) ◽  
pp. 609-625 ◽  
Author(s):  
SHUNSUKE ISHIMITSU ◽  
KENJI TAKAMI ◽  
KOJI SAKAMOTO ◽  
KAZUTOSHI FUKUI
Keyword(s):  
Wavelet Transforms ◽  
Noise Source ◽  
Subjective Evaluation ◽  
Correlation Factor ◽  
Measured Signal ◽  
Time Varying ◽  
Time Analysis ◽  
Complex Time ◽  
Time Frequency ◽  
Dominant Feature

Recent research on time-frequency analysis using wavelet transforms has focused on analyzing wavelets using a mathematical approach. In this study, a measured signal is adopted as the wavelet, and we analyze the correlation between acoustic signals in the car cabin and air intake suction noise signals by applying the proposed system. Since the traditional calculation of correlation repeats the averaging procedure, the original signal must be stationary. Consequently, a technique for separating and identifying noise from each part of the engine is used for analyzing the noise source contribution. To apply the method to time-varying signals, we introduce the concept of an instantaneous correlation factor (ICF), and we prove that the dominant feature of the correlation can be estimated by the ICF. However, the ICF method has not previously used a time-varying analyzing wavelet. In this research, to conduct a practical analysis we proposed Time-Time analysis, which was developed based on the ICF. Furthermore, we also proposed Complex Time-Time analysis, extending into the complex region for improvement in accuracy. First, we verified the effectiveness of the ICF using simulated signals, and then we investigated the contribution of intake noise (which is one of the engine noises) to the car interior noise during acceleration. Using an ICF in which signals relating to each noise source are selected for the analyzing wavelet (AW), we showed that this technique is also useful for contribution analysis. We also conducted a fundamental experiment about audibility impressions.

Spectral Estimation of Noisy Seismogram using Time-Frequency Analyses

2016 ◽  
Vol 7 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Vaneeta Devi ◽  
M. L. Sharma
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Spectral Estimation ◽  
Window Size ◽  
Frequency Component ◽  
Maximum Energy ◽  
Gabor Transform ◽  
Time Frequency ◽  
Signal Features

Time–Frequency analyses have the advantage of explaining the signal features in both time domain and frequency domain. This paper explores the performance of Time–Frequency analyses on noisy seismograms acquired from seismically active region in NW Himalayan. The Short Term Fourier Transform, Gabor Transform, Wavelet Transform and Wigner-Ville Distribution have been used in the present study to carry out Time-Frequency analyses. Parametric study has been carried out by varying basic parameters viz. sampling, window size and types. Wavelet analysis (Continuous Wavelet Transform) has been studied with different type of wavelets. The seismograms have been stacked in time-frequency domain using Gabor Transform and have been converted using Discrete Gabor Expansion techniques. The Spectrograms reveals better spectral estimation in time-frequency domain than Fourier Transform and hence recommended to estimate dominate frequency components, phase marking and timings of phase. The time of occurrence of frequency component corresponding to maximum energy burst can be identified on seismograms

Gear Crack Detection With the Wavelet Transform

1997 ◽  
Author(s):  
D. Boulahbal ◽  
M. F. Golnaraghi ◽  
F. Ismail
Keyword(s):  
Wavelet Transform ◽  
Crack Detection ◽  
Fatigue Cracks ◽  
Vibration Signal ◽  
Frequency Component ◽  
Time Frequency ◽  
Tooth Width ◽  
Ideal Tool ◽  
Short Time ◽  
Gear Crack

Abstract The wavelet transform has the ability to extract global information as well as localized small features from a given signal. This property makes it very well suited to the study of time-varying vibration signals generated by the operation of faulty gears. For a healthy and properly designed gear set, the vibration signal consists mainly of the gear meshing frequency component and its harmonics. Developing fatigue cracks introduce short-time transients that modulate both the amplitude and phase of the otherwise steady vibration signal. These transients are often difficult to detect with the traditional time-only or frequency-only techniques. Being a joint time-frequency distribution, the Wavelet transform allows one to look at the evolution in time of a signal’s frequency content. It thus appears to be the ideal tool to detect the localized transients. In this study, we use both the amplitude and phase maps of the wavelet transform to assess the condition of an instrumented gear test rig. With the proposed technique, simulated cracks as small as 20% of the tooth width at the root are easily detectable.

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