讨论了基于波信号的解调和人工神经网络的损伤识别算法, 以及其在Lamb波信号的应用。Lamb波与损伤相互作用, 将修改回波信号, 从该信息提取相关的损害信息可用于自动损伤检测。然而, 由于该波与损害相互作用的复杂性, 波信号的反应是不容易解释。反应的波信号被认为是一个高频率载波信号调制的低频信号。基线减法后, 频域卷积和滤波, 原来的信号解调成一个新的简单的信号, 其与因损伤发生的能量变化有关。随后进行特征提取, 通过寻找新信号的局部极大值和所取得的峰值和位置将作为人工神经网络的损伤特性的输入。这种损伤检测验证算法的有效性, 通过一个带缺口复合材料层压板缺损模型利用有限元进行验证。对不同缺口深度和位置的反应波信号用于模拟和训练和测试的样本。最后, 对网络的精度和泛化能力进行评估, 结果是令人满意的。
所属栏目
2010年远东无损检测论坛论文精选
收稿日期
作者单位
S. Saravanan:School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore
F. Ju:School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore
N.Q. Guo:School of Engineering, Monash University, Bandar Sunway 46150, Selangor, Malaysia
引用该论文:
S. Saravanan,F. Ju,N.Q. Guo.Wave Signal Demodulation and Artificial Neural Networks for Damage Identification[J].Nondestructive Testing,2010,32(8):584~592
S. Saravanan,F. Ju,N.Q. Guo.波信号的解调和人工神经网络的损伤识别算法[J].无损检测,2010,32(8):584~592
参考文献
【1】
Alleyne DN, Cawley P. The interaction of Lamb waves with defects[J]. IEEE Trans Ultrason Ferroelectr Freq Control, 1992, 39(3): 381-397.
【2】
Guo N, Cawley P. The interaction of Lamb waves with delaminations in composite laminates[J]. J Acoust Soc Am, 1993, 94(4): 2240-2246.
【3】
Guo NQ, Cawley P. Lamb wave reflection for the quick nondestructive evaluation of large composite laminates[J]. Mater Eval, 1994, 52(3): 404-411.
【4】
Alleyne D, Cawley P. The long range detection of corrosion in pipes using Lamb waves[G]. Annual Review of Progress in Quantitative Nondestructive Evaluation. Snowmass Village: Plenum Press Div Plenum Publishing, 1994: 2073-2080.
【5】
Scudder LP, Hutchins DA, Guo NQ. Laser-generated ultrasonic guided waves in fiber-reinforced plates - Theory and experiment[J]. Ieee Transactions on Ultrasonics Ferroelectrics and Frequency Control, 1996, 43(5): 870-880.
【6】
Lemistre M, Balageas D. Structural health monitoring system based on diffracted Lamb wave analysis by multiresolution processing[J].Smart Mater Struct, 2001, 10(3): 504-511.
【7】
Jian XM, Guo N, Li MX, et al. Characterization of bonding quality in a multilayer structure using segment adaptive filtering[J]. Journal of Nondestructive Evaluation, 2002, 21(2): 55-65.
【8】
Bartoli I, FL di Scalea, Fateh M, et al. Modeling guided wave propagation with application to the long-range defect detection in railroad tracks[J]. NDT E Int, 2005, 38(5): 325-334.
【9】
Su ZQ, Ye L. Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm[J]. Compos Struct, 2004, 66(1-4): 627-637 .
【10】
Lu Y, Ye L, Su ZQ, et al. Artificial Neural Network (ANN)-based Crack Identification in Aluminum Plates with Lamb Wave Signals[J]. J Intell Mater Syst Struct, 2009, 20(1): 39-49.
【11】
Kohonen T. The self-organizing map[J]. Proc IEEE, 1990, 78(9): 1464-1480.
【12】
Haykin SS. Neural networks: a comprehensive foundation, Prentice Hall, Upper Saddle River, NJ (1999).
【13】
Zang C, Imregun M. Structural damage detection using artificial neural networks and measured FRF data reduced via principal component protection[J]. J Sound Vibr, 2001, 242(5): 813-827.
【14】
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
【15】
Cybenko G. Approximation by superpositions of a sigmoidal function[J]. Mathematics of Control, Signals, and Systems, 1989, 2(4): 303-314.