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第 40 卷 第 5 期               李娟娟: 小样本字典学习的兰姆波模态识别方法                                           773


                                                                   tions on Ultrasonics, Ferroelectrics, and Frequency Con-
             3 结论                                                  trol, 2014, 61(9): 1515–1524.
                                                                 [9] Ratassepp M, Fan Z, Lasn K. Wave mode extraction
                 本文分析了基于小样本字典学习的兰姆波模                               from multimodal wave signals in an orthotropic composite
             态识别原理和实现流程,将 AAA 板频散特性看作                              plate[J]. Ultrasonics, 2016, 71: 223–230.
                                                                [10] Alleyne D N, Cawley P. The interaction of Lamb waves
             一个线性时不变系统,激励信号作为系统输入,不同
                                                                   with defects[J]. IEEE Transactions on Ultrasonics Ferro-
             模态的兰姆波传播特定距离后得到的信号作为系                                 electrics & Frequency Control, 1992, 39(3): 381–397.
             统输出,建立字典,其中键是模态和距离的组合,值                            [11] Gao F, Zeng L, Lin J, et al. Mode separation in frequency-
             是走时和小波能量的组合;获取待测兰姆波信号的                                wavenumber domain through compressed sensing of far-
                                                                   field Lamb waves[J]. Measurement Science and Technol-
             走时和能量分布,通过查询字典,得到对应的模态和
                                                                   ogy, 2017, 28(7): 075004.
             传播距离,从而识别兰姆波模态,根据能量分布估计                            [12] Harley J B, Moura J M F. Broadband localization in a dis-
             参数后重构各模态信号。对 AAA 板中传播距离为                              persive medium through sparse wavenumber analysis[C]//
             0.3 m、0.5 m 的 A 0 、S 0 模态直达波和反射波进行了                   IEEE International Conference on Acoustics, Speech &
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             模态识别,识别结果准确,并能够准确地重建各个波                            [13] Harley J B. Predictive guided wave models through sparse
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             致谢     衷心感谢中北大学韩焱教授对我的学术                           [14] Xu K, Ta D, Moilanen P, et al. Mode separation of Lamb
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