| 飞机疲劳开裂声发射波形信号的人工神经网络模式识别方法 |
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| 引用本文： | 胡振龙,沈功田,邬冠华,刘时风,吴占稳.飞机疲劳开裂声发射波形信号的人工神经网络模式识别方法[J].无损检测,2012(3):4-7,66. |
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| 作者姓名： | 胡振龙 沈功田 邬冠华 刘时风 吴占稳 |
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| 作者单位： | [1]南昌航空大学无损检测技术教育部重点实验室,南昌330063 [2]中国特种设备检测研究院,北京100013 [3]北京声华兴业科技有限公司,北京100029 |
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| 摘 要： | 利用SOM神经网络,对分类挑选的飞机疲劳过程采集的声发射波形信号进行模式识别分析,得到一组(300个)疑似裂纹的波形信号。其特点有:频谱图上同时出现三个明显的峰值,其能量相对较大,且频率基本固定。其中,第三峰值频率(168.5kHz)与先前的试验数据(175.8kHz)相接近,已具备了较明显的裂纹特征。
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| 关 键 词： | 声发射 波形分析 疲劳裂纹 小波包降噪 SOM神经网络 频谱分析 |
Pattern Recognition of Aircraft Fatigue Cracking Based on Waveform Analysis Method and Artificial Neural Networks of Acoustic Emission Signals |
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| HU Zhen-Long,SHEN Gong-Tian,WU Guan-Hua,LIU Shi-Feng,WU Zhan-Wen.Pattern Recognition of Aircraft Fatigue Cracking Based on Waveform Analysis Method and Artificial Neural Networks of Acoustic Emission Signals[J].Nondestructive Testing,2012(3):4-7,66. |
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| Authors: | HU Zhen-Long SHEN Gong-Tian WU Guan-Hua LIU Shi-Feng WU Zhan-Wen |
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| Affiliation: | 1. Key Lab of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China; 2. China Special Equipment Inspection and Researeh Institute, Beijing 100013, China; 3. Beijing Soundwel Technology Co Ltd. Beijing 100029, China) |
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| Abstract: | In this paper, SOM neural network was used to identify the AE waveforrn signals of aircraft fatigue test. A group of suspected crack signals were acquired. Their characteristics were obtained. Three peaks appear simultaneously in frequency spectrum. Their energies were relatively large and located at same frequency. The frequency of third peak（168. 5 kHz） was consistent with previous result（175.8 kHz）, and already showed obvious characteristics of crack signal. |
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| Keywords: | Acoustic emission Waveform analysis Fatigue crack WPD SOM neural network Spectrum analysis |
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