Feature Extraction and Pattern Recognition of Acoustic Emission Signals Generated from Plywood Damage Based on EMD and Neural Network

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Journal of Vibration and Shock ›› 2012, Vol. 31 ›› Issue (15) : 30-35.

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Feature Extraction and Pattern Recognition of Acoustic Emission Signals Generated from Plywood Damage Based on EMD and Neural Network

XU Feng LIU Yun-fei*

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Abstract

Abstract: Aiming at the non-stationary features of acoustic emission (AE) signals generated from plywood damage and overlapping of damage features in practice, a method of feature extraction and pattern recognition was proposed based on empirical mode decomposition (EMD) and BP neural network. Firstly, the original AE signals were decomposed by EMD, and intrinsic mode function (IMF) including the main feature information were selected. Secondly, energy ratios of IMF were constructed as feature vectors to identify damage signals types. Finally, BP neutral network pattern classifier was established to identify four types of plywood damage signals. The measured data of five-plywood damage show that the method can extract AE signals characteristics precisely and identify damage types efficiently.

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acoustic emission / empirical mode decomposition / neural network / feature extraction / pattern recognition

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XU Feng LIU Yun-fei*. Feature Extraction and Pattern Recognition of Acoustic Emission Signals Generated from Plywood Damage Based on EMD and Neural Network[J]. Journal of Vibration and Shock, 2012, 31(15): 30-35