基于神经网络及数据融合的管道缺陷定量识别

分析了管道缺陷的特征参数与漏磁信号的关系，研究显示管道缺陷的深度和长度分别与漏磁信号的幅值和宽度呈近似线性关系。将实际漏磁信号预处理以消除传感器提离值不同带来的影响，然后用已训练好的BP神经网络进行了管道缺陷的定量识别，识别结果的误差〈10％，完全满足实际检测要求。分别用加权平均和自适应加权平均两种方法将轴向和径向漏磁信号进行信号级融合，融合后基于BP神经网络的缺陷定量识别的精度和可靠性得到了明显提高，其中加权平均法更优。

Quantitative Recognition of Pipeline Defects based on Neural Network and Data Fusion

Quantitative recognition of defect was the difficulty in pipeline magnetic flux leakage(MFL) inspection.The relationship between pipeline defects and MFL signals were studied.The study showed that the depth and length of the defects had an approximately linear relation to the amplitude and width of MFL signals respectively.The real MFL signals were preprocessed to eliminate the effects of sensor lift-off and then were recognized quantitatively by BP neural network already trained.The recognition result error was less than 10%,and practical inspection requirement was completely fulfilled.In order to get better recognition result,the axial and radial MFL signal were fused at signal level by weighted average and adaptive weighted average methods respectively.The accuracy and reliability of quantitative recognition based on BP neural network were improved remarkably after signal fusion.The results showed that the weighted average method was better than the adaptive weighted average method.