Acoustic emission source identification based on harmonic wavelet packet and support vector machine  

基于谐波小波包和支持向量机的声发射源识别(英文)

作　　者：于金涛[1,2] 丁明理[1] 孟凡刚 乔玉良 王祁[1] 

机构地区：[1]哈尔滨工业大学自动化测试与控制系,哈尔滨150001 [2]哈尔滨商业大学计算机与信息工程学院,哈尔滨150028 [3]哈尔滨航空工业集团,哈尔滨150066

出　　处：《Journal of Southeast University(English Edition)》2011年第3期300-304,共5页东南大学学报（英文版）

基　　金：The Natural Science Foundation of Heilongjiang Province ( No. F201018);the National Natural Science Foundation of China( No. 60901042)

摘　　要：In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.为了解决直升机动部件疲劳损伤类型识别问题,提出了一种基于谐波小波包特征提取和层次支持向量多分类器的声发射源类型识别方法.声发射信号经过4层谐波小波包分解后,提取各个频段的能量特征用于声发射源类型识别,克服了传统小波包分析能量泄露、频带选取不灵活、不同层频率分辨率不同的缺点.首先,利用已知声发射源类型的试验数据训练层次支持向量多分类器,然后,利用其余试验数据进行测试.碳纤维材料试件压断试验结果表明:该方法有效地实现了声发射源多类识别,并且在计算效率和识别精度上都优于小波包特征提取方法.

关 键 词：harmonic wavelet packet hierarchy support vector machine acoustic emission source identification 

分 类 号：TG115.28[金属学及工艺—物理冶金]