Application of Wavelet Packet in Defect Recognition of Optical Fiber Fusion Based on ISO14000

Zhen Zhang, Jun-jie Xi
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Abstract

The important meaning of the optical fiber fusion defect recognition was introduced based on ISO14000. Detecting the optical fiber fusion point by using the UltraPAC system, aiming at the defect feature, the method of analyzing and extracting the defect eigenvalue by using wavelet packet analysis and pattern recognition by making use of the wavelet neural network is discussed. This method can realize to extract the interrelated information which can reflect defect feature from the ultrasonic information being detected and analysis it by the information. Constructing the network model for realizing the qualitative recognition of defects. The results of experiment show that the wavelet packet analysis adequately make use of the information in time-domain and in frequency-domain of the defected echo signal, multi-level partition the frequency bands and analyze the high-frequency part further which don’t been subdivided by multi-resolution analysis, and choose the interrelated frequency bands to make it suited with signal spectrum. Thus, the time-frequency resolution is risen, the good local amplificatory property of the wavelet neural network and the study characteristic of multi-resolution analysis can achieve the higher accuracy rate of the qualitative classification of fusion defects.
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小波包在ISO14000光纤融合缺陷识别中的应用
介绍了基于ISO14000标准的光纤熔接缺陷识别的重要意义。利用UltraPAC系统对光纤熔点进行检测,针对缺陷特征,讨论了利用小波包分析提取缺陷特征值和利用小波神经网络进行模式识别的方法。该方法可以实现从被检测的超声信息中提取能反映缺陷特征的相关信息,并利用这些信息进行分析。构建实现缺陷定性识别的网络模型。实验结果表明,小波包分析充分利用了缺陷回波信号的时域和频域信息,对多分辨率分析无法细分的频段进行多级划分,并对高频部分进行进一步分析,选择相互关联的频段,使其与信号频谱相适应。从而提高了时频分辨率,利用小波神经网络良好的局部放大特性和多分辨率分析的研究特点,可以达到较高的融合缺陷定性分类准确率。
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