scholarly journals Finite element simulation of marine generators

2020 ◽  
pp. 92-97
Author(s):  
А.В. Гринек ◽  
А.М. Фищенко ◽  
И.П. Бойчук ◽  
Д.Н. Перелыгин ◽  
Н.В. Савостеенко
Keyword(s):  
Wavelet Transform ◽  
Geometric Model ◽  
Harmonic Component ◽  
Low Frequency ◽  
Linkage Phase ◽  
Maximum Value ◽  
Model Study ◽  
Element Simulation ◽  
Frequency Components ◽  
Transform Coefficients

В статье рассмотрено численное моделирование синхронного генератора. Описана последовательность создания геометрической модели. Представлены результаты численного моделирования статической и динамической задачи. Получены временные осциллограммы потокосцепления, фазных токов и напряжений, сил и моментов. На их основании получены частотные характеристики заданного генератора на холостых режимах. С помощью вейвлет-преобразования проведен анализ переходного процесса. Исследование показало, что существует три частотные области: область нарастания скорости, достижение критической скорости и выход на установившийся режим. Анализ коэффициентов вейвлет-преобразования исследуемого сигнала дал информацию об энергии, содержащейся в соответствующих частотных составляющих ряда. Данная численная модель дает возможность идентифицировать спектры напряжений, токов, сил и моментов, соответствующих механическим и электромагнитным дефектам. Показана возможность диагностирования дефектов генератора, обусловленного эксцентриситетом ротора, с помощью модельного исследования на пусковых режимах. Наличие эксцентриситета ротора приводит к появлению гармонической составляющей в спектре силы большой амплитуды с максимальным значением на низкой частоте. The sequence of creating a geometric model is described. The results of numerical simulation of static and dynamic problems are presented. Time oscillograms of flux linkage, phase currents and voltages, forces and moments were obtained. The analysis of the transient process is carried out using the wavelet transform. The study showed that there are three frequency ranges: the area of increasing speed, reaching critical speed and reaching steady state. Analysis of the wavelet transform coefficients gave information about the energy, which is contained in the corresponding frequency components of the series. This numerical model makes it possible to identify the spectrum of voltages, currents, forces and moments corresponding to mechanical and electromagnetic defects. The possibility of diagnosing the eccentricity of the rotor using a model study in starting modes is shown. Eccentricity leads to the appearance of a harmonic component in the power spectrum with a large amplitude with a maximum value at a low frequency.

THE FLEXIBLE GABOR-WAVELET TRANSFORM FOR CAR CRASH SIGNAL ANALYSIS

2009 ◽  
Vol 07 (04) ◽  
pp. 481-490 ◽  
Author(s):  
DARIAN M. ONCHIŞ ◽  
ESPERANZA M. SÚAREZ SÁNCHEZ
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Processing Technique ◽  
Gabor Wavelet ◽  
Signal Processing Technique ◽  
Gabor Wavelet Transform ◽  
Car Crash ◽  
Time Signals ◽  
Frequency Components ◽  
Short Time

This paper is concerned with the spectral decomposition and the adaptive analysis of data coming from car crash simulations. The mathematical ingredient of the proposed signal processing technique is the flexible Gabor-wavelet transform or the α-transform that reliably detects both high and low frequency components of such complicated short-time signals. We go from the functional treatment of this wavelet-type transform to its numerical implementation and we show how it can be used as an improved tool for spectral investigations compared to the short-time Fourier transform or the classical wavelet transform.

scholarly journals Investigation of the operation of a ship’s synchronous generator based on a numerical model

2021 ◽  
Vol 2061 (1) ◽  
pp. 012004
Author(s):  
AV Grinek ◽  
IP Boychuk ◽  
A M Fishenko ◽  
NV Savosteenko ◽  
O N Gerasimenko
Keyword(s):  
Numerical Model ◽  
Geometric Model ◽  
Harmonic Component ◽  
Low Frequency ◽  
Synchronous Generator ◽  
Diagnostic Model ◽  
Electrical Machine ◽  
Static Mode ◽  
Design Calculations ◽  
Mechanical Defect

Abstract The article presents the results of modeling a ship’s synchronous generator. Based on preliminary design calculations, a geometric model of the generator was built. The static and overclocking processes of the generator are investigated. For static modes, the frequency response of the process is obtained. A numerical model was used to identify a mechanical defect. On the example of a simulated defect of an electrical machine - the eccentricity of the generator rotor, the signature of the defect signal is obtained. The study revealed that the presence of rotor eccentricity leads to the appearance of a harmonic component in the spectrum of the force of large amplitude with a maximum value at a low frequency. Transient analysis was carried out using wavelet transformations. The results of the study of dynamics show how the rotor speed increases. The simulation revealed three frequency regions of the signal under study: the region of the increase in speed, the achievement of the critical speed (between the second and third seconds), and the exit to the steady-state. A diagram of a system for diagnosing defects in an electrical machine using a digital twin - a numerical model is proposed. Generator defects (mechanical and electromechanical) can be identified based on model data. Databases of defect signatures in a static mode and a diagnostic model, which contains algorithms for deciding on the presence of a defect, can serve as the basis of information for an operator to decide.

scholarly journals Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Priyadharsini Ravisankar
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Stationary Wavelet Transform ◽  
Acoustic Image ◽  
Underwater Acoustic ◽  
Acoustic Images ◽  
Frequency Components ◽  
Shrinkage Functions

Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods.

scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

scholarly journals Seismic section image detail enhancement method based on wavelet transform

2019 ◽  
Author(s):  
Xiang-Yu Jia ◽  
Chang-Lei DongYe
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Structural Information ◽  
Low Frequency ◽  
Frequency Noise ◽  
Seismic Section ◽  
Adaptive Enhancement ◽  
Enhancement Method ◽  
Frequency Components ◽  
Texture Filtering

Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. First, the image is wavelet decomposed to obtain a low frequency structural component and a series of high frequency texture detail components; Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail; Finally, the processed high and low frequency components are reconstructed by wavelet can obtained the seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.

scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

The Fusion Algorithm of Infrared and Visible Images Based on Computer Vision

2014 ◽  
Vol 945-949 ◽  
pp. 1851-1855
Author(s):  
Ming Hui Deng ◽  
Jian Xin Kang ◽  
Yan Jun Li
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Source Image ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Second Generation Wavelet Transform ◽  
Fused Image ◽  
Visible Images ◽  
Frequency Components ◽  
Second Generation Wavelet

Directionlet transform is a lattice-based skewed discrete wavelet transform. It has advantages of multi-directional and anisotropy compared with standard two-dimensional wavelet transform, thus, it is better at describing the characteristics of images. For the research focus of different-source image fusion, a novel fusion algorithm based on Directionlet transform was proposed, and the fusion speed was improved efficiently by combing the transform with a lifting scheme. Firstly, between transform direction and alignment direction, two registered source images were decomposed by using lifting Directionlet transform respectively in different times, thus anisotropic sub images were obtained. Then, the low frequency components were combined averagely and the selection principle of high frequency sub images were based on which has stronger anisotropic edge information. Finally, the fused image was obtained by using inverse Directionlet transform. Experimental results show that the fusion effect and speed are both better than standard wavelet transform and other second generation wavelet transform.

3D Fusion Technique Based on Wavelet Transform for 3D Dataset of Gravity and Magnetic Inversion

2014 ◽  
Vol 644-650 ◽  
pp. 3984-3987
Author(s):  
Yuan Jie Li ◽  
Liang Hui Guo ◽  
Guo Li Zhang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Wavelet Decomposition ◽  
Synthetic Data ◽  
Low Frequency ◽  
Fusion Technique ◽  
Gravity And Magnetic ◽  
Magnetic Inversion ◽  
Frequency Components ◽  
Image Fusion Technique

We presented 3D fusion technique based on wavelet transform for analyzing 3D dataset of gravity and magnetic inversion intuitively and comprehensively. The technique expands the conventional 2D image fusion technique based on wavelet transform to 3D case, including using 3D wavelet decomposition and reconstruction to replace 2D ones and reforming the fusion rules of high and low frequency components in 3D field. The disciplines of some crucial parameters related to the 3D fusion technique were provided, so that bring some convenient to use this techinique. The synthetic data test showed that the 3D fusion technique is effetive and reliable.

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