Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction

2002 ◽  
Vol 51 (3) ◽  
pp. 497-508 ◽  
Author(s):  
S.K. Goumas ◽  
M.E. Zervakis ◽  
G.S. Stavrakakis
Keyword(s):  
Feature Extraction ◽  
Wavelet Analysis ◽  
Discrete Wavelet ◽  
Vibration Signals ◽  
Discrete Wavelet Analysis ◽  
Washing Machines

Estimation of moisture and oil content of in-shell nuts with a capacitance sensor using discrete wavelet analysis

2009 ◽  
Author(s):  
Chari V. Kandala ◽  
Jaya Sundaram ◽  
K. N. Govindarajan ◽  
Chris L. Butts ◽  
Jeyam Subbiah
Keyword(s):  
Wavelet Analysis ◽  
Oil Content ◽  
Discrete Wavelet ◽  
Capacitance Sensor ◽  
Discrete Wavelet Analysis

SeismicWaveTool: Continuous and discrete wavelet analysis and filtering for multichannel seismic data

2013 ◽  
Vol 184 (1) ◽  
pp. 162-171 ◽  
Author(s):  
J.J. Galiana-Merino ◽  
J.L. Rosa-Herranz ◽  
S. Rosa-Cintas ◽  
J.J. Martinez-Espla
Keyword(s):  
Wavelet Analysis ◽  
Seismic Data ◽  
Discrete Wavelet ◽  
Discrete Wavelet Analysis ◽  
Multichannel Seismic Data

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

scholarly journals Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3536 ◽  
Author(s):  
Binqiang Chen ◽  
Qixin Lan ◽  
Yang Li ◽  
Shiqiang Zhuang ◽  
Xincheng Cao
Keyword(s):  
Feature Extraction ◽  
Wavelet Analysis ◽  
Low Frequency ◽  
Wavelet Packets ◽  
Discrete Wavelet ◽  
Sinusoidal Wave ◽  
Displacement Signal ◽  
Fault Features ◽  
Reconstructed Signal ◽  
Weak Fault

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features.

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