多传感器检测管道缺陷数据融合方法

Data Fusion Method for Multi-Sensor Detection of Pipeline Defects

针对多传感器管道缺陷检测数据融合精度不高的问题,提出了一种改进鸟群算法(IBSN)与加权正则化极限学习机(WRELM)相结合的多传感器检测管道缺陷数据融合方法。首先,利用电磁超声导波、漏磁以及涡流检测设备采集管道缺陷数据,将高斯核函数样本权重矩阵和正则化参数引入极限学习机中,建立WRELM数据融合模型;而后,通过引入混沌变量和高斯扰动、优化警惕行为以及改变飞行行为中步长因子来优化鸟群算法,采用IBSA优化WRELM输入层到隐含层的连接权值和隐含层的偏置;最后,利用多仪器检测管道缺陷数据融合平台进行实验分析。实验结果表明:采用IBSA-WRELM的多仪器检测管道缺陷数据融合模型的误差最小,仅为2.33%,有效提高了多仪器检测管道缺陷数据的融合精度。

Considering the problem of low fusion accuracy of multisensor pipeline defect detection data,a data fusion method of multiinstrument pipeline defect detection is proposed,which combines the improved bird swarm algorithm(IBSA)with the weighted regularized extreme learning machine(WRELM).First,pipeline defect data are collected using electromagnetic ultrasonic guided wave testing equipment,magnetic flux leakage testing equipment,and eddy current testing equipment.The Gaussian kernel function sample weight matrix and the regularization parameter are subsequently introduced into the extreme learning machine,and the WRELM data fusion model is established.The bird swarm algorithm is then optimized by introducing chaotic variables and Gaussian perturbations,which optimizes vigilance behavior and changes the step factor in the flight behavior.The IBSA is used to optimize the connection weight between the input layer and the hidden layer and the bias of the hidden layer of WRELM.Finally,the data fusion platform for multiinstrument pipeline defect detection is utilized for experimental analysis.The experimental results show that the error of the multiinstrument pipeline defect data fusion model using the IBSA to optimize the WRELM is the smallest at just 2.33%.The fusion accuracy of multiinstrument pipeline defect data is effectively improved.