将比例边界有限元法(Scaled Boundary Finite Element Method,SBFEM)和机器学习算法相结合,基于Lamb波在含缺陷薄板结构中传播时结构的动力响应变化定量反演结构中的缺陷信息。SBFEM通过改变裂纹所在子域的比例中心位置反映不同的裂纹信...将比例边界有限元法(Scaled Boundary Finite Element Method,SBFEM)和机器学习算法相结合,基于Lamb波在含缺陷薄板结构中传播时结构的动力响应变化定量反演结构中的缺陷信息。SBFEM通过改变裂纹所在子域的比例中心位置反映不同的裂纹信息,减少了需要进行重划分的网格数量,大大提升了计算效率,能够提供足够多的反映缺陷特性的训练数据;基于极限学习机的人工神经网络机器学习模型避免了传统反分析问题求解的目标函数极小化迭代过程,能在极快的学习速度下保证缺陷反演的精度。因此,提出的缺陷反演模型大大减少了运算时间成本。若干数值算例表明:建立的反演分析模型能够根据含缺陷的薄板结构Lamb波传播时观测点的动响应信号,准确地探测出薄板结构中的开口裂纹状缺陷的位置和大小等信息,并具有很高的反演效率,且在信号含有噪声的情况下仍具有较好的鲁棒性。展开更多
In this paper, a new method of time reversal for defect diagnosis of concealed structure has been proposed based on the detecting technique of structure acoustic wave and the theory of time reversal. The time reversal...In this paper, a new method of time reversal for defect diagnosis of concealed structure has been proposed based on the detecting technique of structure acoustic wave and the theory of time reversal. The time reversal recurrence formula for detecting the acoustic wave speed constitution of concealed structures with bilevel asynchronous test has been established. The wave speed constitution can be reconstructed in 2 D graticule form by means of this method. The result of model test shows the method is valid.展开更多
In this paper,a modified genetic local search algorithm(MGLSA) is proposed.The proposed algorithm is resulted from employing the simulated annealing technique to regulate the variance of the Gaussian mutation of the g...In this paper,a modified genetic local search algorithm(MGLSA) is proposed.The proposed algorithm is resulted from employing the simulated annealing technique to regulate the variance of the Gaussian mutation of the genetic local search algorithm(GLSA).Then,an MGLSA-based inverse algorithm is proposed for magnetic flux leakage(MFL) signal inversion of corrosive flaws,in which the MGLSA is used to solve the optimization problem in the MFL inverse problem.Experimental results demonstrate that the MGLSA-based inverse algorithm is more robust than GLSA-based inverse algorithm in the presence of noise in the measured MFL signals.展开更多
文摘将比例边界有限元法(Scaled Boundary Finite Element Method,SBFEM)和机器学习算法相结合,基于Lamb波在含缺陷薄板结构中传播时结构的动力响应变化定量反演结构中的缺陷信息。SBFEM通过改变裂纹所在子域的比例中心位置反映不同的裂纹信息,减少了需要进行重划分的网格数量,大大提升了计算效率,能够提供足够多的反映缺陷特性的训练数据;基于极限学习机的人工神经网络机器学习模型避免了传统反分析问题求解的目标函数极小化迭代过程,能在极快的学习速度下保证缺陷反演的精度。因此,提出的缺陷反演模型大大减少了运算时间成本。若干数值算例表明:建立的反演分析模型能够根据含缺陷的薄板结构Lamb波传播时观测点的动响应信号,准确地探测出薄板结构中的开口裂纹状缺陷的位置和大小等信息,并具有很高的反演效率,且在信号含有噪声的情况下仍具有较好的鲁棒性。
文摘In this paper, a new method of time reversal for defect diagnosis of concealed structure has been proposed based on the detecting technique of structure acoustic wave and the theory of time reversal. The time reversal recurrence formula for detecting the acoustic wave speed constitution of concealed structures with bilevel asynchronous test has been established. The wave speed constitution can be reconstructed in 2 D graticule form by means of this method. The result of model test shows the method is valid.
基金the Innovation Program of ShanghaiMunicipal Education Commission(No.09YZ340)the Leading Academic Discipline Project of ShanghaiMunicipal Education Commission(No.J51301)+2 种基金the Special Scientific Research Project of Scienceand Technology Commission of Shanghai Municipality(No.08240512000)the Shanghai Municipal EducationCommission Scientific Foundation Projection(No.06LZ009)the Shanghai Key Science and TechnologyProject(No.061612041)
文摘In this paper,a modified genetic local search algorithm(MGLSA) is proposed.The proposed algorithm is resulted from employing the simulated annealing technique to regulate the variance of the Gaussian mutation of the genetic local search algorithm(GLSA).Then,an MGLSA-based inverse algorithm is proposed for magnetic flux leakage(MFL) signal inversion of corrosive flaws,in which the MGLSA is used to solve the optimization problem in the MFL inverse problem.Experimental results demonstrate that the MGLSA-based inverse algorithm is more robust than GLSA-based inverse algorithm in the presence of noise in the measured MFL signals.
