Selection of suitable mother wavelet along with vanishing moment for the effective detection of crack in a beam

2022 ◽  
Vol 163 ◽  
pp. 108136
Author(s):  
Ramnivas Kumar ◽  
Ravi Nigam ◽  
Sachin K Singh
Keyword(s):  
Mother Wavelet ◽  
Vanishing Moment ◽  
Selection Of

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

2015 ◽  
Vol 40 (45) ◽  
pp. 15823-15833 ◽  
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

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

2019 ◽  
Vol 64 (2) ◽  
pp. 163-176 ◽  
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.

An automatic wavelet denoising scheme for heart sounds

2015 ◽  
Vol 13 (03) ◽  
pp. 1550016 ◽  
Author(s):  
Tahar Omari ◽  
Fethi Bereksi-Reguig
Keyword(s):  
Heart Diseases ◽  
Heart Sounds ◽  
Automated System ◽  
Mother Wavelet ◽  
Electronic Stethoscope ◽  
Novel Approach ◽  
Breath Sounds ◽  
Many Sources ◽  
Pcg Signals ◽  
Selection Of

Phonocardiograms (PCGs), recording of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analyzed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG signal including lung and breath sounds, environmental noise and blood flow noises which are known as murmurs. These murmurs contain many information on heart hemodynamic which can be used particularly in detecting of heart valve diseases. An automated system for heart murmurs processing can be an important tool in diagnostic of heart diseases using a simple electronic stethoscope. However, the first step before developing any automated system is the segmentation of the PCG signals from which the murmurs can be separated. A robust segmentation algorithm must have a robust denoising technique, where, wavelet transform (WT) is among the ones which exhibits very high satisfactory results in such situations. However, the selection of level of decomposition and the mother wavelet are the major challenges. This paper proposes a novel approach for an automatic selection of mother wavelet and level of decomposition that can be used in heart sounds denoising. The obtained results on both simulative and real PCG signals showed that the proposed approach can successfully select the best level of decomposition with the best mother wavelet that can be used in extraction operation of main PCG sound components (S1 and S2) from various types of murmurs.

Selection of a mother wavelet as identification pattern for the detection of cracks in shafts

2021 ◽  
pp. 107754632110260
Author(s):  
Marta Zamorano ◽  
María Jesus Gómez Garcia ◽  
Cristina Castejón
Keyword(s):  
Standard Method ◽  
Wavelet Packet ◽  
Rotation Frequency ◽  
Wavelet Function ◽  
Classification Systems ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Rotating Shaft ◽  
Critical Rotation ◽  
Selection Of

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.

Enhanced ACFM detection performance by multi-parameter synergy analysis

2020 ◽  
Vol 62 (2) ◽  
pp. 81-85
Author(s):  
Junqi Gao ◽  
Lingsi Sun ◽  
Shuxiang Zhao ◽  
Ying Shen
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Performance ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Wavelet Base ◽  
Wavelet Bases ◽  
Base Property ◽  
Signal Characteristics ◽  
Vanishing Moment ◽  
Index Value

A procedure for the enhancement of alternating current field measurement (ACFM) detection performance is proposed based on a multi-parameter synergy analysis (MPSA) algorithm. Firstly, to gain the maximised ACFM signal characteristics, wavelet base property matching is adopted to choose the favourable wavelet bases. To this aim, the following six base properties should be considered: orthogonality, compact support, symmetry, discrete wavelet transform (DWT), vanishing moment and regularity. It is found that the applicable wavelet bases are Haar, Daubechies (DbN), Symlets (SymN) and Coiflets (CoifN). Secondly, the MPSA method is applied to select the optimal mother wavelet candidates. The candidate with the largest MPSA index value is regarded as the optimum wavelet base. Finally, the proposed MPSA denoising strategy is demonstrated using an ACFM experiment. The results indicate that wavelets Db4 with decomposition level (DL)9 and Sym7 with DL8 are most appropriate for x- and z-axis ACFM signal denoising, respectively. The enhanced ACFM detection performance is experimentally verified and it is found that the signal-to-noise ratio (SNR) is increased by 33.8 dB and 26.7 dB for the x- and z-axis signal, respectively.

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