基于智能算法的涡流检测自然裂纹形状重构

Reconstruction of Natural Crack Shape from ECT Signals by Using Intelligent Algorithm

人工制作了疲劳裂纹试样,利用一种小波分析方法对采集的疲劳裂纹涡流检测(ECT)信号进行了去噪预处理及信号特征提取,通过破坏性检测方法获得了裂纹的真实形状.在建立疲劳裂纹参数化模型的基础上,利用经过处理的裂纹ECT信号与裂纹形状参数样本库对径向基函数(RBF)神经网络进行训练.采用遗传算法,通过创建大量表示裂纹形状参数个体的初始种群,输入经过训练的神经网络,得到对应的ECT预测信号;然后运用改进的遗传策略进行迭代反演优化,对裂纹形状最优解进行搜索.重构结果表明该方法具有快速、精确的优点.

By using the artificially-fabricated fatigue crack samples as the research objects, the eddy cureent testing （ECT） signals of fatigue crack are collected and are then denoised by means of wavelet transform, with the signal feature being also extracted. Afterwards, a destructive testing procedure is performed to obtain the true profiles of the crack. Based on a parametric model of the fatigue crack, the radial basis function （RBF） neural network is trained with the preprocessed ECT signals and crack shape parameters. Moreover, a great deal of initial populations of crack shape parameters are generated as the inputs of the trained RBF neural network. Thus, the predicted ECT signals, namely the outputs of the network are obtained. An improved genetic strategy is finally applied to the iterative inversion optimization to search the optimal crack shape. Reconstruction results show that the proposed method is of high speed and precision.