High-Sensitivity Ultrasonic Guided Wave Monitoring of Pipe Defects Using Adaptive Principal Component Analysis.
High-Sensitivity Ultrasonic Guided Wave Monitoring of Pipe Defects Using Adaptive Principal Component Analysis.
复制标题
使用自适应主成分分析对管道缺陷进行高灵敏度超声波导波监测
DOI：
10.3390/s21196640
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发表时间：
2021-10-06
期刊：
影响因子：
--
通讯作者：
Zheng Y
中科院分区：
文献类型：
--
作者：
Ma J;Tang Z;Lv F;Yang C;Liu W;Zheng Y;Zheng Y
超声导波监测常用于工业管道的结构健康监测，但由于环境和管道结构对导波信号的影响，小缺陷难以识别。提出了一种基于自适应主元分析（APCA）的管道缺陷高灵敏度监测算法，该算法计算了信号的灵敏度指标，优化了主元分析（PCA）中主元的选取过程。通过提取信号的子空间特征，建立了一个综合损伤指数K，直观地显示了缺陷的存在。利用采集的几种管道类型的数据集对该损伤监测算法进行了测试，实验结果表明，APCA方法可以监测到直管上截面损失率为0.075%、螺旋管上截面损失率为0.15%、弯管上截面损失率为0.18%的孔洞缺陷，该方法上级诸如最佳基线减法（OBS）和平均欧几里得距离（AED）的常规方法。该算法得到的损伤指数曲线结果清晰地显示了缺陷的变化趋势;而且K指数的贡献率大致显示了缺陷的位置。
Ultrasonic guided wave monitoring is regularly used for monitoring the structural health of industrial pipes, but small defects are difficult to identify owing to the influence of the environment and pipe structure on the guided wave signal. In this paper, a high-sensitivity monitoring algorithm based on adaptive principal component analysis (APCA) for defects of pipes is proposed, which calculates the sensitivity index of the signals and optimizes the process of selecting principal components in principal component analysis (PCA). Furthermore, we established a comprehensive damage index (K) by extracting the subspace features of signals to display the existence of defects intuitively. The damage monitoring algorithm was tested by the dataset collected from several pipe types, and the experimental results show that the APCA method can monitor the hole defect of 0.075% cross section loss ratio (SLR) on the straight pipe, 0.15% SLR on the spiral pipe, and 0.18% SLR on the bent pipe, which is superior to conventional methods such as optimal baseline subtraction (OBS) and average Euclidean distance (AED). The results of the damage index curve obtained by the algorithm clearly showed the change trend of defects; moreover, the contribution rate of the K index roughly showed the location of the defects.
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影响因子：
4.1
作者：
Konstantinidis, G.;Drinkwater, B. W.;Wilcox, P. D.
通讯作者：
Wilcox, P. D.
影响因子：
0.4
作者：
Bertoncini, Francesco;Cappelli, Mauro;Raugi, Marco
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Raugi, Marco
影响因子：
20.6
作者：
Dobson, Jacob;Cawley, Peter
通讯作者：
Cawley, Peter
影响因子：
4.2
作者：
Lu, YH;Michaels, JE
通讯作者：
Michaels, JE
影响因子：
4.2
作者：
Hayashi, T;Tamayama, C;Murase, M
通讯作者：
Murase, M