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管道腐蚀缺陷超声信号的PSO-SVM模式识别研究

潘峰 唐东林 陈印 吴薇萍 丁超

潘峰, 唐东林, 陈印, 吴薇萍, 丁超. 管道腐蚀缺陷超声信号的PSO-SVM模式识别研究[J]. 机械科学与技术, 2020, 39(5): 751-757. doi: 10.13433/j.cnki.1003-8728.20190197
引用本文: 潘峰, 唐东林, 陈印, 吴薇萍, 丁超. 管道腐蚀缺陷超声信号的PSO-SVM模式识别研究[J]. 机械科学与技术, 2020, 39(5): 751-757. doi: 10.13433/j.cnki.1003-8728.20190197
Pan Feng, Tang Donglin, Chen Yin, Wu Weiping, Ding Chao. Ultrasonic Signal Pattern Recognition of Pipeline Corrosion Defects with PSO-SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(5): 751-757. doi: 10.13433/j.cnki.1003-8728.20190197
Citation: Pan Feng, Tang Donglin, Chen Yin, Wu Weiping, Ding Chao. Ultrasonic Signal Pattern Recognition of Pipeline Corrosion Defects with PSO-SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(5): 751-757. doi: 10.13433/j.cnki.1003-8728.20190197

管道腐蚀缺陷超声信号的PSO-SVM模式识别研究

doi: 10.13433/j.cnki.1003-8728.20190197
基金项目: 

西南石油大学国家重点实验室项目 PLN201828

成都市技术创新研发项目 2018-YF05-00201-GX

四川省科技支撑项目 2017FZ0033

详细信息
    作者简介:

    潘峰(1994-), 硕士研究生, 研究方向为模式识别, 1274319142@qq.com

    通讯作者:

    唐东林, 教授, 博士生导师, 博士, tdl840451816@163.com

  • 中图分类号: TE973

Ultrasonic Signal Pattern Recognition of Pipeline Corrosion Defects with PSO-SVM

  • 摘要: 针对金属管道腐蚀问题,提出了一种基于支持向量机(Support vector machine,SVM)与粒子群优化(Particle swarm optimization,PSO)相结合的管道腐蚀缺陷的分类方法。对预处理后的超声缺陷信号进行经验模态分解(Empirical mode decomposition,EMD),提取相应的时域无量纲参数作为特征向量;建立SVM缺陷分类模型,并采用PSO算法优化SVM参数,提高模型的缺陷分类准确率。实验证明,该方法建立的模型针对不同深度的超声缺陷信号的识别率达到87.5%,优于相同试验样本下BP神经网络和RBF神经网络的分类准确率。
  • 图  1  PSO-SVM算法流程图

    图  2  实验装置和实验缺陷样本

    图  3  无缺陷和3种含缺陷信号的时域图

    图  4  第25号缺陷样本超声信号的EMD分解效果图

    图  5  粒子群适应度曲线

    图  6  测试样本分类结果

    表  1  时域无量纲参数

    No. 统计参数 公式
    1 斜度
    2 峰度
    3 峰值指标
    4 清除指标
    5 形状指标
    6 脉冲指标
    下载: 导出CSV

    表  2  缺陷样本详情

    缺陷腐蚀面积/mm 缺陷腐蚀形状 缺陷最大横截面积/mm2
    无缺陷 - -
    2 圆形 40
    5 矩形 80
    8 无规则形状1 120
    无规则形状2 160
    无规则形状3
    无规则形状4
    下载: 导出CSV

    表  3  缺陷回波信号数据集分布

    缺陷腐蚀面积/mm 缺陷样本数量 样本信号数据数量 样本信号数据编号
    无缺陷 24 240 1~240
    2 24 240 241~480
    5 24 240 480~720
    8 24 240 720~960
    下载: 导出CSV

    表  4  测试集识别结果

    缺陷实际情况
    无缺陷 2 mm 5 mm 8 mm 识别率/%
    测试集预测结果 无缺陷 60 0 0 0 87.5
    2 mm 0 58 9 9
    5 mm 0 1 44 3
    8 mm 0 1 7 48
    敏感度/% 100 96.67 73.33 80
    下载: 导出CSV

    表  5  不同寻优算法性能对比

    寻优算法 识别率/% 耗时/s
    GA 86.67(208/240) 84.15
    GS 86.25(207/240) 73.63
    PSO 87.50(210/240) 53.47
    下载: 导出CSV

    表  6  不同模型的分类性能对比

    模型 识别率/% 耗时/s
    BP-NN 78.33(188/240) 47.55
    RBF-NN 74.17(178/240) 43.97
    PSO-SVM 87.50(207/240) 53.47
    下载: 导出CSV
  • [1] Barbian A, Beller M. In-line inspection of high pressure transmission pipelines: State of the art and future trends[C]//18th World Conference on Nondestructive Testing. Durban, South Africa, 2012: 16-20 https://www.researchgate.net/publication/265999709_In-Line_Inspection_of_High_Pressure_Transmission_Pipelines_State-of-the-Art_and_Future_Trends
    [2] Duisterwinkel E H A, Talnishnikh E, Krijnders D, et al. Sensor motes for the exploration and monitoring of operational pipelines[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(3):655-666 doi: 10.1109/TIM.2017.2775404
    [3] 车红昆, 吕福在, 项占琴.超声检测信号多特征SVM-Bayes融合识别[J].振动与冲击, 2011, 30(12):265-269 doi: 10.3969/j.issn.1000-3835.2011.12.053

    Che H K, Lv F Z, Xiang Z Q. Ultrasonic signal recognition by multiple features SVM-Bayes fusion method[J]. Journal of Vibration and Shock, 2011, 30(12):265-269(in Chinese) doi: 10.3969/j.issn.1000-3835.2011.12.053
    [4] Meng M, Chua Y J, Wouterson E, et al. Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks[J]. Neurocomputing, 2017, 257:128-135 doi: 10.1016/j.neucom.2016.11.066
    [5] 黄晶, 阙沛文.小波分析在管道缺陷超声检测中的应用[J].传感技术学报, 2003, 16(3):263-266 doi: 10.3969/j.issn.1004-1699.2003.03.007

    Huang J, Que P W. The application of wavelet analysis in ultrasonic testing of pipeline defect[J]. Journal of Transcluction Technology, 2003, 16(3):263-266(in Chinese) doi: 10.3969/j.issn.1004-1699.2003.03.007
    [6] 丁攀, 吕福在, 项占琴.基于小波包分解和支持向量机的石油套管缺陷智能识别[J].钢铁研究学报, 2012, 24(5):58-62 http://d.old.wanfangdata.com.cn/Periodical/gtyjxb201205012

    Ding P, Lv F Z, Xiang Z Q. Intelligent flaws identification method for oil casing pipe based on wavelet packet decomposition and support vector machine[J]. Journal of Iron and Steel Research, 2012, 24(5):58-62(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/gtyjxb201205012
    [7] 赵永林, 刘桂雄, 周德光, 等.应用经验模式分解法处理超声无损检测信号[J].现代制造工程, 2006(4):90-92 doi: 10.3969/j.issn.1671-3133.2006.04.032

    Zhao Y L, Liu G X, Zhou D G, et al. Ultrasonic nondestructive test signal processing based on empirical mode decomposition[J]. Modern Manufacturing Engineering, 2006(4):90-92(in Chinese) doi: 10.3969/j.issn.1671-3133.2006.04.032
    [8] 李秋锋, 黄攀, 施倩, 等.基于经验模态分解去噪的粗晶材料超声检测[J].应用基础与工程科学学报, 2014, 22(3):566-573 http://www.cnki.com.cn/Article/CJFDTotal-YJGX201403015.htm

    Li Q F, Huang P, Shi Q, et al. Ultrasonic testing of coarse-grained materials based on EMD denoising method[J]. Journal of Basic Science and Engineering, 2014, 22(3):566-573(in Chinese) http://www.cnki.com.cn/Article/CJFDTotal-YJGX201403015.htm
    [9] 陈天璐, 阙沛文, 金涛, 等.管道缺陷多超声传感器检测数据的神经网络融合[J].传感技术学报, 2004, 17(3):403-406 doi: 10.3969/j.issn.1004-1699.2004.03.012

    Chen T L, Que P W, Jin T, et al. Neural network-style fusion of pipelines' defect data scanned by multi-ultrasonic sensors[J]. Journal of Transcluction Technology, 2004, 17(3):403-406(in Chinese) doi: 10.3969/j.issn.1004-1699.2004.03.012
    [10] 唐东林, 魏子兵, 潘峰, 等.基于PCA和SVM的管道腐蚀超声内检测[J].传感技术学报, 2018, 31(7):1040-1045 doi: 10.3969/j.issn.1004-1699.2018.07.011

    Tang D L, Wei Z B, Pan F, et al. Ultrasonic internal detection of pipeline corrosion based on PCA and SVM[J]. Chinese Journal of Sensors and Actuators, 2018, 31(7):1040-1045(in Chinese) doi: 10.3969/j.issn.1004-1699.2018.07.011
    [11] 戴波, 赵晶, 周炎.基于支持向量机的管道腐蚀超声波内检测[J].化工学报, 2008, 59(7):1812-1817 doi: 10.3321/j.issn:0438-1157.2008.07.038

    Dai B, Zhao J, Zhou Y. Ultrasonic in-line inspection of pipeline corrosion based on support vector machine[J]. Journal of Chemical Industry and Engineering (China), 2008, 59(7):1812-1817(in Chinese) doi: 10.3321/j.issn:0438-1157.2008.07.038
    [12] 刘清坤, 阙沛文, 郭华伟, 等.基于支持向量机和特征选择的超声缺陷识别方法研究[J].中国机械工程, 2006, 17(1):9-12 doi: 10.3321/j.issn:1004-132X.2006.01.003

    Liu Q K, Que P W, Guo H W, et al. Support vector machine and chaos-genetic optimization based ultrasonic flaw identification[J]. China Mechanical Engineering, 2006, 17(1):9-12(in Chinese) doi: 10.3321/j.issn:1004-132X.2006.01.003
    [13] 熊庆, 张卫华.基于MF-DFA与PSO优化LSSVM的滚动轴承故障诊断方法[J].振动与冲击, 2015, 34(11):188-193 http://d.old.wanfangdata.com.cn/Periodical/zdycj201511033

    Xiong Q, Zhang W H. Rolling bearing fault diagnosis method using MF-DFA and LSSVM based on PSO[J]. Journal of Vibration and Shock, 2015, 34(11):188-193(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zdycj201511033
    [14] 姜久亮, 刘文艺, 侯玉洁, 等.基于内积延拓LMD及SVM的轴承故障诊断方法研究[J].振动与冲击, 2016, 35(6):104-108 http://d.old.wanfangdata.com.cn/Periodical/zdycj201606019

    Jiang J L, Liu W Y, Hou Y J, et al. Bearing fault diagnosis based on integral waveform extension LMD and SVM[J]. Journal of Vibration and Shock, 2016, 35(6):104-108(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zdycj201606019
    [15] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995 doi: 10.1098/rspa.1998.0193
    [16] Tsao W C, Li Y F, Du Le D, et al. An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis[J]. Measurement, 2012, 45(6):1489-1498 doi: 10.1016/j.measurement.2012.02.030
    [17] 徐卓飞, 刘凯, 张海燕, 等.基于经验模式分解和主元分析的滚动轴承故障诊断方法研究[J].振动与冲击, 2014, 33(23):133-139 http://d.old.wanfangdata.com.cn/Periodical/zdycj201423025

    Xu Z F, Liu K, Zhang H Y, et al. A fault diagnosis method for rolling bearings based on empirical mode decomposition and principal component analysis[J]. Journal of Vibration and Shock, 2014, 33(23):133-139(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zdycj201423025
    [18] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297 http://d.old.wanfangdata.com.cn/Periodical/hwyhmb200803006
    [19] Moosavian A, Khazaee M, Najafi G, et al. Spark plug fault recognition based on sensor fusion and classifier combination using Dempster-Shafer evidence theory[J]. Applied Acoustics, 2015, 93:120-129 doi: 10.1016/j.apacoust.2015.01.008
    [20] 温正, 孙华克.MATLAB智能算法[M].北京:清华大学出版社, 2017

    Wen Z, Sun H K. MATLAB intelligent algorithm[M]. Beijing:Tsinghua University Press, 2017(in Chinese)
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  • 收稿日期:  2019-05-05
  • 刊出日期:  2020-05-05

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