The Feature Extraction of Water Stress AE Signal on Seedlings Based on Wavelet Analysis

2011 ◽  
Author(s):  
Yang Liu ◽  
Zhang Junmei ◽  
Luo Xiaoli ◽  
Kan Jiangming ◽  
Yang Kai
Keyword(s):  
Feature Extraction ◽  
Water Stress ◽  
Wavelet Analysis ◽  
Ae Signal

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

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.

scholarly journals Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Long Han ◽  
Chengwei Li ◽  
Liqun Shen
Keyword(s):  
Feature Extraction ◽  
Simulation Experiment ◽  
Rolling Bearing ◽  
Multiscale Entropy ◽  
Similarity Measurement ◽  
Damage And Fracture ◽  
Fault Features ◽  
Information Method ◽  
Ae Signal ◽  
Multivariate Multiscale Entropy

Due to the powerful ability of EEMD algorithm in noising, it is usually applied to feature extraction of fault signal of rolling bearing. But the selective correctness of sensitive IMF after decomposition can directly influence the correctness of feature extraction of fault signal. In order to solve the problem, the paper firstly proposes a new method on selecting sensitive IMF based on Cloud Similarity Measurement. By comparing this method in simulation experiment with the traditional mutual information method, it is obvious that the proposed method has overcome the misjudgment in the traditional method and it has higher accuracy, by factually collecting the normal, damage, and fracture fault AE signal of the inner ring of rolling bearing as samples, which will be decomposed by EEMD algorithm in the experiments. It uses Cloud Similarity Measurement to select sensitive IMF which can reflect the fault features. Finally, it sets the Multivariate Multiscale Entropy (MME) of sensitive IMF as the eigenvalue of original signal; then it is classified by the SVM to determine the fault types exactly. The results of the experiments show that the selected sensitive IMF based on Cloud Similarity Measurement is effective; it can help to improve the accuracy of the fault diagnosis and feature extraction.

Research and Analysis on Rock Acoustic Emission Wave Characteristics

2014 ◽  
Vol 638-640 ◽  
pp. 534-537
Author(s):  
Jian Ping He
Keyword(s):  
Acoustic Emission ◽  
Wavelet Analysis ◽  
Laboratory Tests ◽  
Low Frequency ◽  
Development Trend ◽  
Signal Wave ◽  
Original Signal ◽  
Great Efficiency ◽  
Wave Characteristics ◽  
Ae Signal

Analyzing wave behavior on acoustic emission laboratory tests from relationship between stress and AE rate , the rock AE signal wave in laboratory is decomposed into high and low frequency elements. Analysis and compare with the details elements include, the approximation elements and original AE wave, the results show that rock AE wave characteristics are not same in the course of transform fracture on stages, found AE wave characteristics storehouse, it is a matter of great significance for utilizing monitoring and prediction regularity and development trend in the course of fracture transform. The error between the original signal and the signal of wave coefficient of the approximation elements reconstructed is minor to, wavelet analysis makes accuracy and reliability of rock AE wave characteristics monitoring and prediction improved, States that wavelet analysis is a great efficiency method.

Experimental Study of Mine AE Signal Based on Wavelet Analysis

2011 ◽  
Vol 148-149 ◽  
pp. 1127-1130 ◽  
Author(s):  
Xiu Zhi Cheng ◽  
Zhen Yu ◽  
Guang Zhu
Keyword(s):  
Experimental Study ◽  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Activity Patterns ◽  
Audio Signal ◽  
P Wave ◽  
Audio Signal Processing ◽  
Ae Signal

Because the wavelet transform can characterize the local signals in time and frequency domain, in the coal mine’s sound signals’ process, an audio signal processing based on wavelet analysis is proposed, the audio signal P wave is isolated and determined by wavelet transform, at the same time, the earthquake source can be located. Through the research of the mine AE signal’s activity patterns, the sound monitoring technology to forecast the mine power disaster is achieved.

Wavelet analysis of hyperspectral reflectance data for spectral feature extraction

2005 ◽  
Author(s):  
Guiling Sun ◽  
Yonghua Fang ◽  
Cuilan Zhang ◽  
Xianbing Wang ◽  
Benyong Yang
Keyword(s):  
Feature Extraction ◽  
Wavelet Analysis ◽  
Spectral Feature ◽  
Hyperspectral Reflectance ◽  
Reflectance Data
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