Wavelet Denoising Based on Genetic Algorithm

Matti, Majd Salah; Al-Sulaifanie, Ahmed Khorsheed

doi:10.1109/icoase.2018.8548814

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Vibration signals are frequently affected by diverse sources and levels of noise, which can significantly hinder the extraction of useful information from the signal. Among the various denoising techniques, wavelet-based denoising has demonstrated excellent performance in various applications [27][28][29][30] due to (i) its inherent capability to handle both stationary and nonstationary signals and (ii) its bank filter-based computation. The steps that describe this process are as follows:…”

Section: Wavelet Denoisingmentioning

confidence: 99%

“…Once the threshold estimator has been selected, either hard or soft thresholding can be applied [27][28][29][30]. 4.…”

Section: Wavelet Denoisingmentioning

confidence: 99%

“…Finally, the inverse DWT is used to reconstruct the denoised signal using the newly thresholded coefficients. Further details can be found in [27][28][29][30].…”

Section: Wavelet Denoisingmentioning

confidence: 99%

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Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals

Granados-Lieberman,

Huerta-Rosales,

Gonzalez-Cordoba

et al. 2023

Applied Sciences

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Transformers are vital elements in electrical networks, but they are prone to various faults throughout their service life. Among these, a winding short-circuit fault is of particular concern to researchers, as it is a crucial and vulnerable component of the transformers. Therefore, if this fault is not addressed at an early stage, it can increase costs for users and affect industrial processes as well as other electrical machines. In recent years, the analysis of vibration signals has emerged as one of the most promising solutions for detecting faults in transformers. Nonetheless, it is not a straightforward process because of the nonstationary properties of the vibration signals and their high-level noise, as well as their different features when the transformer operates under different conditions. Based on the previously mentioned points, the motivation of this work is to contribute a methodology that can detect different severities of short-circuited turns (SCTs) in transformers in both transient and steady-state operating regimes using vibration signals. The proposed approach consists of a wavelet-based denoising stage, a short-time Fourier transform (STFT)-based analysis stage for the transient state, a Fourier transform (FT)-based analysis stage for the steady-state, the application of two fault indicators, i.e., the energy index and the total harmonic distortion index, and two neural networks for automatic diagnosis. To evaluate the effectiveness of the proposed methodology, a modified transformer is used to experimentally reproduce different levels of SCTs, i.e., 0-healthy, 5, 10, 15, 20, 25, and 30 SCTs, in a controlled way. The obtained results show that the proposed approach can detect the fault condition, starting from an initial stage for consolidation and a severe stage to accurately assess the fault severity, achieving accuracy values of 90%.

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Section: Wavelet Denoisingmentioning

confidence: 99%

“…Once the threshold estimator has been selected, either hard or soft thresholding can be applied [27][28][29][30]. 4.…”

Section: Wavelet Denoisingmentioning

confidence: 99%

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Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals

Granados-Lieberman,

Huerta-Rosales,

Gonzalez-Cordoba

et al. 2023

Applied Sciences

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“…The aim of GA is the iterative search among the generated solutions of the considered problem. This strategy leads to the determination of the fittest generation (Ławrynowicz, 2011;Wojarnik, 2015;Awange, et al, 2018;Matti & Khorsheed Al-Sulaifanie, 2018). It can be noticed that the idea of using the genetic algorithm and the decision process determine one direction -choosing the best alternative from all of the possible alternatives.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm as Automated Valuation Model Component in Real Estate Investment Decisions System

Chmielewska

Adamiczka²,

Romanowski

2020

Real Estate Management and Valuation

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Every real-estate related investment decision making process calls for the careful analysis of available information even though it is often carried out in conditions of uncertainty. The paper attempts to minimize the impact of the factor on the quality of real estate investment decisions through the proposal of application of tools based on the simulation of the process of natural selection and biological evolution. The aim of the study is to analyze the potential of methodology based on genetic algorithms (GA) to build automated valuation models (AVM) in uncertainty conditions and support investment decisions on the real estate market. The developed model facilitates the selection of properties adequate to the adopted assumptions, i.e. individuals best suited to the environment. The tool can be used by real estate investment advisors and potential investors on the market to predict future processes and the proper confrontation of past events with planned events. Even though genetic algorithms are tools that have already found particular application on real estate market, there are still areas that need further studies in the case of more effective uses. The obtained results allow for the possibilities and barriers of applying GA to real estate market analyses to be defined.

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“…Some of these methods are wavelet threshold processing developed by Donoho and Johnstone [15] and their variants (e.g., VisuShrink [16], SureShrink [17], and BayersShrink [18]), different methods are used to determine the wavelet threshold parameters. Matti and Al-Sulaifanie combined genetic algorithm (GA) with wavelet transform (WT) for signal denoising by using the parameters of WT as the input of GA and the output of MSE as the fitness value [19]. Reference [20] used differential evolution (DE) to estimate the best parameter set for wavelet shrinkage denoising.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Cao

Jia

Zhao

et al. 2022

Mathematical Problems in Engineering

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Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.

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Wavelet Denoising Based on Genetic Algorithm

Cited by 8 publications

References 23 publications

Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals

Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals

Genetic Algorithm as Automated Valuation Model Component in Real Estate Investment Decisions System

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

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