Acoustic Emission Source Identification Based on Pattern Recognition Method

Zhigang Feng, Jiu Yao

Abstract


A new pattern recognition method based on harmonic wavelet packet (HWPT) and hierarchy support vector machine (H-SVM) is proposed to solve the fatigue damage identification problem of helicopter component. In this approach, HWPT is used to extract the energy feature of acoustic emission (AE) signals on different frequency bands and to reduce the dimensionality of original data features. The H-SVM classifier is used to identify the AE source type. A subset of the experimental data for known AE source type is used to train the H-SVM classifier, the remaining set of data is used to test the H-SVM classifier. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The results indicate that the proposed approach can implement AE source type identification effectively, and has better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.3587


Keywords


Harmonic wavelet packet; Support vector machine; RBF neural network; Acoustic emission; Pattern recognition

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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