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基于速度误差校正的林木应力波无损检测断层成像算法

焦治 李光辉 武夕

焦治, 李光辉, 武夕. 基于速度误差校正的林木应力波无损检测断层成像算法[J]. 北京林业大学学报, 2018, 40(1): 108-119. doi: 10.13332/j.1000-1522.20170384
引用本文: 焦治, 李光辉, 武夕. 基于速度误差校正的林木应力波无损检测断层成像算法[J]. 北京林业大学学报, 2018, 40(1): 108-119. doi: 10.13332/j.1000-1522.20170384
Jiao Zhi, Li Guang-hui, Wu Xi. Tomography imaging algorithm based on velocity error correction for stress wave nondestructive evaluation of wood[J]. Journal of Beijing Forestry University, 2018, 40(1): 108-119. doi: 10.13332/j.1000-1522.20170384
Citation: Jiao Zhi, Li Guang-hui, Wu Xi. Tomography imaging algorithm based on velocity error correction for stress wave nondestructive evaluation of wood[J]. Journal of Beijing Forestry University, 2018, 40(1): 108-119. doi: 10.13332/j.1000-1522.20170384

基于速度误差校正的林木应力波无损检测断层成像算法

doi: 10.13332/j.1000-1522.20170384
基金项目: 

江苏省重点研发计划项目 BE2016627

中央高校基本科研业务费专项 RP51635B

无锡市国际科技研发合作项目 CZE02H1706

江苏省普通高校专业学位研究生实践创新计划项目 SJLX16_0499

国家自然科学基金项目 61472368

详细信息
    作者简介:

    焦治。主要研究方向:智能无损检测技术。Email:novrule@163.com 地址: 214122 江苏省无锡市江南大学物联网工程学院

    责任作者:

    李光辉,博士,教授。主要研究方向:传感器网络、容错计算和智能无损检测技术。Email: ghli@jiangnan.edu.cn 地址:同上

  • 中图分类号: S781;TS67

Tomography imaging algorithm based on velocity error correction for stress wave nondestructive evaluation of wood

  • 摘要: 目的应力波断层成像技术已经在林木无损检测领域得到了广泛的应用, 然而木材的各向异性特征以及应力波速度反演计算的误差对断层成像的精度影响较大。因此,进一步提高断层图像的准确性非常关键。方法根据传统的应力波速度反演原理, 提出了一种基于速度误差校正的断层成像算法(ECIA)。该算法利用最小二乘QR分解法(LSQR)计算应力波在林木横截面网格单元内的速度分布, 并使用误差校正机制(ECM)优化断层图像。为了评估算法的性能, 分别选取了若干实验室内的原木试样及扬州市区古树进行无损检测实验, 利用德国PICUS应力波断层成像仪获取的应力波传播数据实现了ECIA算法, 生成了各实验样本的断层图像。结果ECIA算法较为精确地检测出了原木及活树中缺陷区域的位置及大小, 尤其在活树的健康检测中, ECIA算法对于缺陷区域尤其是轻微腐朽区域的检测精度高于PICUS检测仪。结论ECIA算法能够产生较为准确的林木断层图像,适用于林木应力波无损检测。

     

  • 图  1  树木横截面内应力波传播示意图

    S代表应力波发射端传感器的位置,R1R2代表应力波接收端传感器的位置;VRVT分别表示径向传播速度和弦向传播速度;β表示弦向角。

    Figure  1.  Stress wave propagation in the cross-section of tree trunk

    S is a source sensor, and R1 and R2 are receiver sensors. VT is the tangential velocity and VR is the radial velocity. β is the angle between radial direction and tangential direction.

    图  2  树木横截面网格单元划分示意图

    SR分别代表应力波发射端传感器和应力波接收端传感器的位置;1, 2, …, M代表网格单元编号;P1, P2, P3, P4代表传播路径与网格单元的交点;dij代表第i条路径被第j个网格单元分割的距离。

    Figure  2.  Grid cells of a cross-section of a tree trunk

    S is a source sensor, and R is a receiver sensor. 1, 2, …, M represent the No. of grid cells. P1, P2, P3, P4 mean the intersections of stress wave paths and grid cells. dij is segment intercepted by the ith wave path on the jth cell.

    图  3  树干横截面内网格单元的分类

    网格单元内的变量或数字代表该单元的编号。

    Figure  3.  Grid cell classification of a cross-section of a tree trunk

    The variable or number represent the No. of grid cells.

    图  4  活树应力波无损检测现场

    Figure  4.  Stress wave testing of a live standing tree

    图  5  试样1的断层图像比较

    Figure  5.  Comparison of tomography images of sample 1

    图  6  试样2的断层图像比较

    Figure  6.  Comparison of tomography images of sample 2

    图  7  试样3的断层图像比较

    Figure  7.  Comparison of tomography images of sample 3

    图  8  试样4的断层图像比较

    Figure  8.  Comparison of tomography images of sample 4

    图  9  试样4的阻力曲线图

    Figure  9.  Resistance curves of sample 4

    图  10  试样5的断层图像比较

    Figure  10.  Comparison of tomography images of sample 5

    图  11  试样5的阻力曲线图

    Figure  11.  Resistance curves of sample 5

    图  12  试样6的断层图像比较

    Figure  12.  Comparison of tomography images of sample 6

    图  13  试样6的阻力曲线图

    Figure  13.  Resistance curves of sample 6

    图  14  试样7的断层图像比较

    Figure  14.  Comparison of tomography images of sample 7

    图  15  试样7的阻力曲线图

    Figure  15.  Resistance curves of sample 7

    图  16  试样8的断层图像比较

    Figure  16.  Comparison of tomography images of sample 8

    图  17  试样8的阻力曲线图

    Figure  17.  Resistance curves of sample 8

    图  18  试样9的断层图像比较

    Figure  18.  Comparison of tomography images of sample 9

    图  19  试样9的阻力曲线图

    Figure  19.  Resistance curves of sample 9

    表  1  原木断层成像结果的定量评价

    Table  1.   Quantitative evaluation to tomography images of logs

    试样编号
    No. of
    sample
    试样横截面积
    Cross-sectional area
    of the sample/cm2
    真实S
    Actual S/cm2
    真实RS
    Actual RS/%
    ECIAPICUS
    S/cm2RS/%ΔRS/%S/cm2RS/%ΔRS/%
    1812.593 5297.284 136.584 6324.558 839.941 1-9.174 6232.514 728.613 921.787 6
    2794.226 078.019 29.823 3102.108 912.856 4-30.876 6285.921 436.000 0-266.475 6
    3804.247 70000088.467 211.000 0>0
    注:S代表试样或断层图像中的缺陷面积;RS代表试样或断层图像中缺陷面积与横截面积的比值;ΔRS代表断层图像的RS与真实RS的相对误差。Notes:S is the defective area, RS is the ratio of the defective area to the cross-section area, and ΔRS is the relative error between the RS of ECIA or PICUS and the actual RS.
    下载: 导出CSV

    表  2  活树断层成像结果的定量评价

    Table  2.   Quantitative evaluation to tomography images of standing trees

    试样编号
    Sample No.
    路径
    Path
    真实RL
    Actual RL
    ECIAPICUS
    RLΔRLRLΔRL
    42-1017.910 417.254 13.664 412.537 329.999 9
    2-913.432 812.455 27.277 70100.000 0
    5-15.970 16.275 8-5.120 518.209 0-205.003 2
    53-888.549 687.042 31.702 267.605 623.652 3
    4-783.333 377.500 07.000 053.928 635.285 7
    5-1074.626 876.417 9-2.400 168.656 77.999 9
    69-175.555 593.043 5-23.145 943.478 342.455 1
    2-1184.444 488.888 9-5.263 288.888 9-5.263 2
    71-789.937 193.548 4-4.015 495.913 9-6.645 5
    2-975.496 781.073 5-7.386 856.989 224.514 3
    4-1091.463 493.566 7-2.299 680.444 412.047 4
    85-93.125 013.437 5-330.000 014.375 0-360.000 0
    3-93.703 74.521 1-22.069 812.592 6-240.000 0
    910-632.558 131.395 33.571 527.665 615.027 0
    10-527.907 027.702 30.733 536.511 6-30.833 1
    7-1246.774 248.387 1-3.448 343.037 77.988 4
    注:RL代表试样或断层图像中路径上缺陷区域的长度与整个路径长度的比值;ΔRL代表断层图像的RL与真实RL的相对误差。Notes: RL is the ratio of the defective length to the whole length of the path under test, and ΔRL is the relative error between the RL of ECIA or PICUS and the actual RL.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-10-23
  • 修回日期:  2017-11-23
  • 刊出日期:  2018-01-01

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