基于BP神经网络的在役管线焊缝故障缺陷的分类识别

本文利用计算机辅助进行在役管线焊故障缝缺陷检测,在缺陷特征提取中提出圆形度、长宽比、填充度、尖部尖锐度、对称度、灰度比以及缺陷的重心坐标相对焊缝中心的位置等7个参数作为缺陷的特征值,可有效地分类识别不同故障缺陷。在缺陷分类的解决方案上,采用具有自组织、自适应的3层前馈式神经网络,运用改进的BP算法,以焊缝缺陷的特征参数作为神经网络的训练样本。本文还通过实验的方法,分析了初始权值、隐含层的神经元数量、动量系数、误差水平及学习速率对网络训练的影响。

Weld Defect Classification and Recognition of the In-service Pipeline Based on BP Neural Network

This paper utilizes the computer to assist in the weld defect detection of the in-service pipeline,in the defect feature,degree of circularity,length-width ratio,compactedness,sharpness of the nose,degree of symmetry、gray scale ratio and the position of the defect’s barycentric coordinates relative to weld center etc.seven parameters are extracted as defect feature,so that different fault defect can be classified and recognized.In the solution of the defect classification and recognition,this paper adopts the self-organized and self-adaptive-3 layers back propagation neural network,applies modified BP algorithm,and takes weld defect feature parameters as training sample of neural network.The network’s hidden nodes number,momentum coefficient,error level and step length etc.network parameters can be obtained optimum values by experimental method.Finally,weld defect classification and recognition achieved effectively.