基于粒子群支持向量机的钢板损伤位置识别

Identification of Steel Plate Damage Position Based on Particle Swarm Support Vector Machine

基于光纤布拉格光栅(FBG)构建的传感器网络,将粒子群(PSO)算法与最小二乘支持向量机(LSSVM)相结合,应用于304钢板损伤识别研究中。以FBG中心波长变化量的信息特征为输入量,钢板结构损伤位置为输出量,构建基于LSSVM的损伤识别预测模型,并与相同条件下构建的反向传播(BP)神经网络预测模型进行对比验证。采用PSO算法优化LSSVM损伤识别模型的核函数参数σ和正则化参数γ,实现钢板结构的损伤位置识别。在300mm×300mm×1mm钢板实验区域,对34组样本进行了损伤位置识别测试。结果表明,33组损伤位置得到了准确识别,准确率达97.06%。这表明PSO优化后的LSSVM的损伤识别预测模型具有自诊断功能。

Based on the fiber network built by fiber Bragg grating（FBG）,the PSO algorithm combined with the least squares support vector machine（LSSVM）was applied to the damage identification problem of 304 steel plate.Information feature of FBG center wavelength variation was used as input quantity and the damage location of steel plate structure was used as output quantity.LSSVM-based damage identification prediction model was constructed.The model was compared with the back-propagation（BP）neural network prediction model constructed under the same conditions.Damage location of steel plate structure was identified by kernel parameterσand regularization parameterγof LSSVM damage identification model,which was optimized by PSO algorithm.In the experimental area of 300 mm×300 mm×1 mm steel plate,34 groups of samples were tested for damage location identification.The results show that the injury position of 33 groups is accurately identified in the 34 samples,and the accuracy rate is 97.06%.The PSO-based LSSVM damage prediction model has a self-diagnostic function.