Damage Detection in Beams using Spatial Fourier Analysis and Neural Networks
Damage Detection in Beams using Spatial Fourier Analysis and Neural Networks
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DOI：
10.1177/1045389x06066292
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发表时间：
2007-04
影响因子：
2.7
通讯作者：
P. Pawar;Kanchi Venkatesulu Reddy;R. Ganguli
中科院分区：
文献类型：
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作者：
P. Pawar;Kanchi Venkatesulu Reddy;R. Ganguli
本研究利用傅立叶分析法，探讨损伤对固定边界条件下梁的影响。利用有限元模型对损伤后的固-固梁进行模态分析，利用空间傅里叶级数展开损伤模态，研究损伤对谐波的影响。这种方法与傅立叶分析在振动问题中的典型时域应用形成对比。研究发现，损伤引起的傅立叶系数的振型，这是敏感的损伤的大小和位置的显着变化。因此，傅立叶系数的向量形式的损伤指数制定。以傅立叶系数为输入，训练神经网络，对损伤位置和大小进行检测。数值研究表明，基于傅立叶系数和神经网络的损伤检测方法能够准确地检测出结构的损伤位置和损伤大小。最后，在噪声存在下的方法的性能进行了研究，它被发现，该方法表现令人满意的存在一些噪声的数据。
This study investigates the effect of damage on beams with fixed boundary conditions using Fourier analysis of mode shapes in the spatial domain. A finite element model is used to obtain the mode shapes of a damaged fixed—fixed beam, and the damaged mode shapes are expanded using a spatial Fourier series and the effect of damage on the harmonics is investigated. This approach contrasts with the typical time domain application of Fourier analysis for vibration problems. It is found that damage causes considerable change in the Fourier coefficients of the mode shapes, which are found to be sensitive to both damage size and location. Therefore, a damage index in the form of a vector of Fourier coefficients is formulated. A neural network is trained to detect the damage location and size using Fourier coefficients as input. Numerical studies show that damage detection using Fourier coefficients and neural networks has the capability to detect the location and damage size accurately. Finally, the performance of the method in the presence of noise is studied and it is found that the method performs satisfactorily in the presence of some noise in the data.