CNN BASED TOOL WEAR CLASSIFICATION USING EMITTED AE SIGNAL WITH EMPIRICAL MODE DECOMPOSITION

2018 ◽  
Vol 18 (7) ◽  
pp. 1015-1025
Author(s):  
J. Emerson Raja ◽  
M. N. Ervina Efzan ◽  
Jakir Hossen ◽  
P. Velrajkumar ◽  
V. Sivaraman
Keyword(s):  
Tool Wear ◽  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Ae Signal ◽  
Tool Wear Classification

Feature Extraction of the Crack AE Signal Using HHT Transform

2013 ◽  
Vol 340 ◽  
pp. 441-444
Author(s):  
K.F. He ◽  
Z.J. Zhang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Crack Depth ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Ae Signal

The use of Hilbert-Huang transform (Hilbert-Huang transform, HHT) on crack AE signal study, through empirical mode decomposition (empirical mode decomposition, EMD) AE signal is decompose into a number of intrinsic mode functions (Intrinsic mode Function, IMF), Hilbert spectrum and Hilbert marginal spectrum are calculated. The results show that crack depth structure bearing of acoustic emission are detected accurately by the number of acoustic emission events, time and crack the degree from Hilbert spectrum and Hilbert marginal spectrum.

scholarly journals Classification of acoustic emission signals in wood damage and fracture process based on empirical mode decomposition, discrete wavelet transform methods, and selected features

2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Meilin Zhang ◽  
Qinghui Zhang ◽  
Junqiu Li ◽  
Jiale Xu ◽  
Jiawen Zheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Damage And Fracture ◽  
Mode Decomposition ◽  
Ae Signal ◽  
Ae Signals

AbstractThe nondestructive testing technology of generated acoustic emission (AE) signals for wood is of great significance for the evaluation of internal damages of wood. To achieve more accurate and adaptive evaluation, an AE signals classification method combining the empirical mode decomposition (EMD), discrete wavelet transform (DWT), and linear discriminant analysis (LDA) classifier is proposed. Five features (entropy, crest factor, pulse factor, margin factor, waveform factor) are selected for classification because they are more sensitive to the uncertainty, complexity, and non-linearity of AE signals generated during wood fracture. The three-point bending load damage experiment was implemented on sample wood of beech and Pinus sylvestris to generate original AE signals. Evaluation indexes (precision, accuracy, recall, F1-score) were adopted to assess the classification model. The results show that the ensemble classification accuracies of two tree species reach 94.58% and 90.58%, respectively. Moreover, compared with the results of the original AE signal, the accuracy of the AE signal processed by the methods proposed is increased by 27.68%. It indicates that the EMD and DWT signal processing methods and selected features improve the classification accuracy, and this automatic classification model has good AE signal recognition performance.

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