Selection of Optimum Mother Wavelet Function for Turkish Phonemes

Nowadays, there are many methods to detect and diagnose defects in mechanical components during operation. The newest methods that can be found in the literature are based on intelligent classification systems and evaluation of patterns to obtain a diagnosis; however, there is not any standard method to assess features. Wavelet packet transform allows to obtain interesting patterns for evaluating the condition of rotating elements. To perform this calculation, it is necessary to select a series of parameters that affect the resulting pattern. These parameters are the decomposition level and the mother wavelet function. A detailed methodology for the selection of the mother wavelet is proposed, which is the aim of this work, to obtain the most suitable patterns in the diagnostic task. This proposed methodology is applied to data obtained from a rotating shaft with a crack located at the change of section. These signals were measured at low rotation frequency (below the critical rotation frequency) and without eccentricity, where detection becomes more complex.

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The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Sensors ◽

10.3390/s21051851 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1851

Author(s):

Young In Jang ◽

Jae Young Sim ◽

Jong-Ryul Yang ◽

Nam Kyu Kwon

Keyword(s):

Wavelet Decomposition ◽

Doppler Radar ◽

Signal To Noise Ratio ◽

Wavelet Function ◽

Mother Wavelet ◽

Decomposition Level ◽

Snr Efficiency ◽

Frequency Features ◽

Selection Of

The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

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Selection of mother wavelet and decomposition level for energy management in electrical vehicles including a fuel cell

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2015.06.055 ◽

2015 ◽

Vol 40 (45) ◽

pp. 15823-15833 ◽

Cited By ~ 23

Author(s):

Mona Ibrahim ◽

Samir Jemei ◽

Geneviève Wimmer ◽

Nadia Yousfi Steiner ◽

Célestin C. Kokonendji ◽

...

Keyword(s):

Fuel Cell ◽

Energy Management ◽

Mother Wavelet ◽

Decomposition Level ◽

Electrical Vehicles ◽

Selection Of

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Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1738 ◽

2014 ◽

Vol 599-601 ◽

pp. 1738-1744

Author(s):

Kai Zhao ◽

Ben Wei Li ◽

Jing Chen

Keyword(s):

Experimental Verification ◽

Wavelet Packet ◽

Vibration Signal ◽

Wavelet Function ◽

Threshold Function ◽

Vibration Signals ◽

Selection Of

Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

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Optimal level and order detection in wavelet decomposition for PCG signal denoising

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0001 ◽

2019 ◽

Vol 64 (2) ◽

pp. 163-176 ◽

Cited By ~ 2

Author(s):

Mohamed Rouis ◽

Abdelkrim Ouafi ◽

Salim Sbaa

Keyword(s):

Signal To Noise Ratio ◽

Optimal Level ◽

Signal Denoising ◽

Discrete Wavelet ◽

Clinical Environment ◽

Mother Wavelet ◽

New Approach ◽

The Mean ◽

Pcg Signals ◽

Selection Of

Abstract The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

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