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钢板缺陷识别的Volterra-SVM模型研究

邓勇 黄远伟 赖治屹

邓勇, 黄远伟, 赖治屹. 钢板缺陷识别的Volterra-SVM模型研究[J]. 机械科学与技术, 2023, 42(1): 132-138. doi: 10.13433/j.cnki.1003-8728.20200590
引用本文: 邓勇, 黄远伟, 赖治屹. 钢板缺陷识别的Volterra-SVM模型研究[J]. 机械科学与技术, 2023, 42(1): 132-138. doi: 10.13433/j.cnki.1003-8728.20200590
DENG Yong, HUANG Yuanwei, LAI Zhiyi. Study on Volterra-SVM Model for Defect Recognition of Steel Plate[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 132-138. doi: 10.13433/j.cnki.1003-8728.20200590
Citation: DENG Yong, HUANG Yuanwei, LAI Zhiyi. Study on Volterra-SVM Model for Defect Recognition of Steel Plate[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 132-138. doi: 10.13433/j.cnki.1003-8728.20200590

钢板缺陷识别的Volterra-SVM模型研究

doi: 10.13433/j.cnki.1003-8728.20200590
基金项目: 

四川省科技支撑计划项目 2017FZ0033

详细信息
    作者简介:

    邓勇(1972-), 高级工程师, 硕士生导师, 研究方向为模式识别和超声无损检测, 201231010013@swpu.edu.cn

  • 中图分类号: TB551;TP391.4

Study on Volterra-SVM Model for Defect Recognition of Steel Plate

  • 摘要: 针对钢板缺陷识别问题, 结合超声波脉冲反射原理, 提出一种基于Volterra级数和支持向量机的钢板缺陷识别方法。首先, 利用Volterra级数模型建立起钢板缺陷的特征模型; 其次, 使用分数阶粒子群优化算法提取出原始信号中的特征参数, 即Volterra级数时域核; 最后, 将提取到的特征向量输入支持向量机模型进行训练与测试, 完成对钢板缺陷的分类识别。设计实验得到多组数据样本, 进行模型验证, 实验结果表明: 基于Volterra级数和支持向量机的识别模型能够较好的完成对钢板缺陷的分类识别, 识别准确率达93.3%。
  • 图  1  脉冲反射原理示意图

    图  2  部分人工缺陷示意图

    图  3  超声检测实验装置图

    图  4  原始信号图

    图  5  FO-PSO适应度值曲线

    图  6  部分缺陷类型的三维曲面图

    图  7  径向基核函数SVM预测结果

    表  1  不同种类缺陷

    序号 大小/mm 形状 深度/mm
    1 6×6 圆形 2
    2 6×6 圆形 3
    3 6×6 矩形 2
    4 12×12 圆形 2
    5 12×12 圆形 3
    6 12×12 矩形 2
    7 6×12 不规则形 2
    8 6×12 不规则形 3
    9 无缺陷 - -
    下载: 导出CSV

    表  2  不同模型分类性能对比

    模型 耗时/s 训练准确率/% 测试准确率/%
    SVM 67.3 100 93.3
    BP-NN 54.6 97.8 84.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-17
  • 刊出日期:  2023-01-25

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