Feature extraction using wavelet packets strategy

2004 ◽  
Author(s):  
Hai Jiang ◽  
Meng Joo Er ◽  
Yang Gao
Keyword(s):  
Feature Extraction ◽  
Wavelet Packets

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.

Research on Acoustic Emission Test Method for Metal Fatigue Fracture Based on Wavelet Packets Feature Extraction

2011 ◽  
Vol 488-489 ◽  
pp. 432-435
Author(s):  
Qi Wang ◽  
Yin Sheng Chen ◽  
Kai Song
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Fatigue Crack ◽  
Crack Propagation ◽  
Fatigue Fracture ◽  
Fatigue Crack Propagation ◽  
Acoustic Emission Signal ◽  
Wavelet Packets ◽  
Metal Fatigue ◽  
Emission Signal

The appearance and growth of the microcracks in the structure is an important factor that influences the structure safety and its service life. Thus it is very important to detect the crack and monitor its growth at the beginning of the crack. Aiming at the main style of failures in metal structure - fatigue fracture, this paper research acoustic emission waveforms analysis that base on wavelet packets feature extraction, through processing acoustic emission signal to test metal fatigue fracture. First, this paper analyses the reason of metal fatigue fracture and introduces the theory of acoustic emission. Based on that, we establish the time domain module of acoustic emission signal and extract the feature of acoustic emission signal using wavelet packets. According to the experimental results bending specimen, acoustic emission techniques monitoring fatigue crack propagation is certificated not only to resemble variable rule of fatigue crack propagation but also to catch generation of fatigue crack in real time. Compared with the method of parameter extraction, this method can not only realize real-time and dynamic monitoring, but also get the result that is similar with fatigue crack expanding rate curve.

scholarly journals Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

2017 ◽  
Vol 7 (4) ◽  
pp. 390 ◽  
Author(s):  
Ming-ai Li ◽  
Wei Zhu ◽  
Hai-na Liu ◽  
Jin-fu Yang
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Wavelet Packets ◽  
Adaptive Feature Extraction

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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