A selection method of Acoustic Emission characteristic parameters based on mutual information and distance measurement

2012 ◽  
Author(s):  
Hongfang Yuan ◽  
Changdui Zhu ◽  
Xi Cao ◽  
Xuewei Wang ◽  
Huaqing Wang
Keyword(s):  
Acoustic Emission ◽  
Mutual Information ◽  
Selection Method ◽  
Distance Measurement ◽  
Emission Characteristic ◽  
Characteristic Parameters

Crack Position Recognition of Metal Deep Drawing Parts Based on BP Neural Network

2013 ◽  
Vol 318 ◽  
pp. 108-113
Author(s):  
Ji Yong Xu ◽  
Jun Li Zhao ◽  
Bing Zhao ◽  
Ying Qing Shao
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Bp Neural Network ◽  
Deep Drawing ◽  
Emission Characteristic ◽  
Characteristic Parameters ◽  
Forming Process ◽  
Crack Location ◽  
Location Identification ◽  
Crack Position

Crack position of metal drawing parts molding was analyzed by the BP neural network. First analysis of the drawing parts forming process may crack in different position. The BP neural network location identification was introduced in the basic process. 11 characteristic parameters from the drawing parts may crack position were gathered by acoustic emission signal acquisition system of deep drawing process. Then the BP neural network was designed rational, and carried out appropriate conduct to train and test. Establishing deep drawing parts of the relations between the different positions crack acoustic emission characteristic parameters and the corresponding position. Crack location was identified, in order to achieve the purpose of positioning the work piece forming process. The better method of acoustic emission location issues are resolved, metal deep drawing forming of crack location identification for basis. Provide the basis for metal drawing parts forming crack location identification.

scholarly journals Acoustic Emission Experimental Research of the Damage Characteristics of Raw Coal under Different Loading and Unloading Rates

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Minbo Zhang ◽  
Li Cui ◽  
Wenjun Hu ◽  
Jinlei Du ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Practical Significance ◽  
Emission Characteristic ◽  
Strain Rates ◽  
Characteristic Parameters ◽  
Energy Increase ◽  
Damage Models ◽  
Ae Energy ◽  
Ae Counts ◽  
Cumulative Ae Energy

In this study, triaxial load failure experiments of coal samples under different strain rates and different confining pressure unloading rates were carried out using an RTX-1000 rock triaxial apparatus, and the acoustic emission characteristic parameters of a Micro-II acoustic emission imaging acquisition instrument were used to study the acoustic emission characteristics and damage deformation law of coals under different conditions. Damage models were constructed on the basis of the characteristic parameters to analyze the damage law of coal samples. Experimental results show that the acoustic emission (AE) counts and AE energy of the coal samples decrease, but the peak AE counts and peak AE energy increase with the increase in strain rates. The cumulative AE counts decrease from 9902 times to 6899 times, the peak counts increase from 209 times to 431 times, the cumulative AE energy decreases from 6986 aJ to 3786 aJ, and the peak AE energy increases from 129 aJ to 312 aJ. The overall level of the AE count rates and the AE energy of the coal samples decrease, but the peak AE counts and peak AE energy increase with the increase in unloading rates. The cumulative AE counts decrease from 18,689 times to 16,842 times, the peak AE count rates increase from 245 times/s to 535 times/s, the cumulative AE energy decreases from 9846 aJ to 7430 aJ, and the peak energy increases from 257 aJ to 587 aJ. The damage models are constructed on the basis of AE counts, and the comparative experimental and theoretical curves are analyzed to obtain a higher fitness close to 1. The damage threshold increases from 0.30 to 0.50 and from 0.34 to 0.55, and the damage amount increases from 0.50 to 0.60 and from 0.34 to 0.62 with the increase in strain rates and unloading rates. The research results have practical significance for revealing the mechanism of disaster occurrence in actual engineering excavation and proposing engineering measures to prevent coal rock damage and disaster occurrence.

Feature Selection Method Based on Mutual Information and Support Vector Machine

2021 ◽  
pp. 2150021
Author(s):  
Gang Liu ◽  
Chunlei Yang ◽  
Sen Liu ◽  
Chunbao Xiao ◽  
Bin Song
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Standard Data ◽  
Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

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