College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China.
Sensors (Basel). 2021 Oct 6;21(19):6640. doi: 10.3390/s21196640.
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.
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，小缺陷很难被识别。本文提出了一种基于自适应主成分分析（APCA）的用于管道缺陷的高灵敏度监测算法，该算法计算信号的灵敏度指标，并优化主成分分析（PCA）中主成分的选择过程。此外，我们通过提取信号的子空间特征来建立一个综合损伤指数（K），直观地显示缺陷的存在。通过对几种类型的管道采集的数据进行测试，实验结果表明，APCA 方法可以监测直管道 0.075%截面损失比（SLR）的孔缺陷、螺旋管道 0.15% SLR 的缺陷和弯管 0.18% SLR 的缺陷，优于最优基线减法（OBS）和平均欧几里得距离（AED）等传统方法。该算法得到的损伤指数曲线的结果清晰地显示了缺陷的变化趋势；此外，K 指数的贡献率大致显示了缺陷的位置。
