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    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

    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。

       

      Abstract:
      ObjectiveStress wave tomography imaging technology has been popularly applied in wood nondestructive evaluation for many years. However, the anisotropy property of wood and the error of stress wave velocity inversion have great impact on the accuracy of tomography imaging. Therefore, it is important to improve the accuracy of the tomography image.
      MethodBased on the traditional stress wave velocity inversion principle, this paper proposes a tomography imaging algorithm (ECIA) with velocity error correction mechanism. The proposed algorithm computes the wave velocity distribution of the grid cells of the wood cross-section by the least square QR decomposition (LSQR) iterative inversion, and then optimizes the tomography with a velocity error correction mechanism (ECM). To evaluate the performance of the proposed algorithm, several healthy and defective logs and ancient live trees in Yangzhou City of Jiangsu Province, southern China were selected as experimental samples, and the nondestructive testing procedures were finished. With the stress wave velocity data sets measured by PICUS equipment, ECIA algorithm was implemented, and the tomography images of log samples and live trees were generated.
      ResultECIA algorithm detected the decay area accurately for log samples and live tree samples. ECIA algorithm had a higher accuracy than PICUS especially for the slight decay.
      ConclusionECIA algorithm can generate accurately tomography images and be used for wood nondestructive evaluation.

       

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