论文信息 - PATTERN RECOGNITION STUDY OF ACOUSTIC EMISSION SIGNALS OF AIRCRAFT FATIGUE CRACKING BASED ON WAVEFORM ANAL- YSIS AND ARTIFICIAL NEURAL NETWORKS

PATTERN RECOGNITION STUDY OF ACOUSTIC EMISSION SIGNALS OF AIRCRAFT FATIGUE CRACKING BASED ON WAVEFORM ANAL- YSIS AND ARTIFICIAL NEURAL NETWORKS

In this paper, SOM neural network was used to identify the AE signal waveforms of aircraft fatigue test, which produced a group of suspected crack signals. Three peaks of relatively large energy appear simultaneously in frequency spectra. The frequency of peak 3 (168.5 kHz) was consistent with previous result (175.8 kHz), showing characteristics of crack AE signal.

Gong-Tian Shen | Zhen-Long Hu | Guan-Hua Wu | Shi-Feng Liu | Zhan-Wen Wu

[1] Li Chun,et al. INVESTIGATION OF ARTIFICIAL NEURAL NETWORK PATTERN RECOGNITION OF ACOUSTIC EMISSION SIGNALS FOR PRESSURE VESSELS , 2001 .

[2] Shen Gong-tian,et al. ACOUSTIC EMISSION SIGNAL PROCESSING TECHNIQUE BASED ON WAVEFORM ANALYSIS , 2002 .

[3] António Marques,et al. Health monitoring of FRP using acoustic emission and artificial neural networks , 2008 .

[4] Jianfei Feng. AE Characteristic Analysis in Aircraft Fatigue Test Under Flight Loading Condition , 2010 .

[5] Dong-Ho Lee,et al. In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network , 2008 .