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RWESOS-VPMCD方法对超声缺陷信号的识别研究

唐东林 陈印 潘峰 李龙 谢光磊

唐东林, 陈印, 潘峰, 李龙, 谢光磊. RWESOS-VPMCD方法对超声缺陷信号的识别研究[J]. 机械科学与技术, 2021, 40(7): 1072-1078. doi: 10.13433/j.cnki.1003-8728.20200169
引用本文: 唐东林, 陈印, 潘峰, 李龙, 谢光磊. RWESOS-VPMCD方法对超声缺陷信号的识别研究[J]. 机械科学与技术, 2021, 40(7): 1072-1078. doi: 10.13433/j.cnki.1003-8728.20200169
TANG Donglin, CHEN Yin, PAN Feng, LI Long, XIE Guanglei. Research on Recognition of Ultrasonic Defect Signal by RWESOS-VPMCD Method[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(7): 1072-1078. doi: 10.13433/j.cnki.1003-8728.20200169
Citation: TANG Donglin, CHEN Yin, PAN Feng, LI Long, XIE Guanglei. Research on Recognition of Ultrasonic Defect Signal by RWESOS-VPMCD Method[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(7): 1072-1078. doi: 10.13433/j.cnki.1003-8728.20200169

RWESOS-VPMCD方法对超声缺陷信号的识别研究

doi: 10.13433/j.cnki.1003-8728.20200169
基金项目: 

四川省科技支撑项目 2017FZ0033

成都市技术创新研发项目 2018-YF05-00201-GX

西南石油大学国家重点实验室项目 PLN201828

详细信息
    作者简介:

    唐东林(1970-), 教授, 博士生导师, 研究方向为无损检测技术、光机电一体化技术, tdl840451816@163.com

  • 中图分类号: TG115.28

Research on Recognition of Ultrasonic Defect Signal by RWESOS-VPMCD Method

  • 摘要: 在通过特征值间的内在关系建立预测模型的变量预测模式识别方法(VPMCD)中, 传统判别方法受特征向量中的个别特征预测异常值影响大, 易导致分类错误。提出基于比值加权的最小误差平方和的判别函数(RWESOS), 可将异常预测的特征权重大幅降低, 提升正确预测特征的权重, 从而提高分类准确率。实验表明, 在对不同缺陷大小的超声检测信号的识别中, 使用RWESOS判别函数的RWESOS-VPMCD方法的识别率比BP神经网络和普通判别函数的VPMCD方法的识别率分别提高了4%和11%。
  • 图  1  人工缺陷板

    图  2  缺陷超声信号采集系统关系图

    图  3  缺陷超声信号采集系统实物图

    图  4  RWESOS-VPMCD分类结构框图

    图  5  基于RWESOS判别的RWESOS-VPMCD方法分类结果

    图  6  BP神经网络分类结果

    图  7  基于常规判别的ELM-VPMCD方法分类结果

    图  8  常规判别法对误差平方矩阵处理

    图  9  RWESOS判别法对误差平方矩阵处理

    图  10  RWESOS判别对常规判别的纠正样本

    图  11  两种判别下各特征权值

    图  12  两种判别方式中异常预测特征的权值

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出版历程
  • 收稿日期:  2020-01-20
  • 刊出日期:  2021-07-01

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