IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Sep;69(9):2681-2695. doi: 10.1109/TUFFC.2022.3195933. Epub 2022 Aug 26.
Temperature changes are a major challenge in outdoor guided wave structural health monitoring of rails. Temperature variations greatly impact the waveform of guided wave signals, making it challenging to diagnose and characterize defects. Traditional temperature compensation methods, such as signal stretch and scale transform, are restricted to use in regular structures, such as plates and pipes. To solve the temperature compensation problem in long rails with serious mode conversion and complex structure echo, we propose a temperature compensation and defect monitoring method, namely, sliding window dynamic time-series warping (SWDTW), which overcomes the challenges of mass computation and overcompensation of dynamic time-series warping (DTW). The basic idea of SWDTW is to utilize sliding windows to accelerate the computation and identify defects from subsequence scales. Then, an index, window subsequence Teager energy (WSTE), is used to indicate the local abnormality of guided wave signals, and a sliding window net (SWnet) is devised to monitor the occurrence of defects automatically. Outdoor monitoring of turnout rails showed that the proposed method can effectively reduce the temperature noise and recognize an artificial defect with 1.16% and 0.36% cross-sectional change rates (CSCRs) on the switch and stock rails, respectively, at different temperatures; moreover, the defect signals processed by SWDTW showed better defect identification performance than those processed by scale transform and DTW.
温度变化是钢轨外导波结构健康监测的主要挑战。温度变化会极大地影响导波信号的波形，导致缺陷诊断和特征描述变得困难。传统的温度补偿方法，如信号拉伸和比例变换，仅限于在规则结构（如板和管）中使用。为了解决长钢轨中严重的模式转换和复杂结构回波的温度补偿问题，我们提出了一种温度补偿和缺陷监测方法，即滑动窗口动态时间序列弯曲（SWDTW），克服了动态时间序列弯曲（DTW）的大规模计算和过补偿的挑战。SWDTW 的基本思想是利用滑动窗口来加速计算，并从子序列尺度识别缺陷。然后，利用窗口子序列 Teager 能量（WSTE）作为指标来指示导波信号的局部异常，并设计了滑动窗口网络（SWnet）来自动监测缺陷的发生。转辙器轨的户外监测结果表明，该方法可以有效地降低温度噪声，并在不同温度下分别识别出开关轨和轨枕上截面变化率（CSCR）为 1.16%和 0.36%的人工缺陷；此外，SWDTW 处理后的缺陷信号比经过比例变换和 DTW 处理后的缺陷信号具有更好的缺陷识别性能。
