Sliding Window Dynamic Time-Series Warping-Based Ultrasonic Guided Wave Temperature Compensation and Defect Monitoring Method for Turnout Rail Foot.
Sliding Window Dynamic Time-Series Warping-Based Ultrasonic Guided Wave Temperature Compensation and Defect Monitoring Method for Turnout Rail Foot.
复制标题
基于滑窗动态时间序列翘曲的道岔轨脚超声导波温度补偿及缺陷监测方法。
DOI：
10.1109/tuffc.2022.3195933
复制
发表时间：
2022
影响因子：
3.6
通讯作者：
Fuzai Lv
中科院分区：
文献类型：
--
作者：
Zhifeng Tang;Junwang Ma;Weixu Liu;Pengfei Zhang;Fuzai Lv
温度变化是室外导波钢轨结构健康监测的主要挑战。温度变化极大地影响导波信号的波形，使得诊断和表征缺陷变得困难。传统的温度补偿方法，例如信号拉伸和尺度变换，仅限于在板和管等常规结构中使用。针对模式转换严重、结构回波复杂的长轨温度补偿问题，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规整（SWDTW），克服了动态时间序列规整（DTW）的海量计算和过度补偿的挑战。 SWDTW的基本思想是利用滑动窗口来加速计算并识别子序列尺度的缺陷。然后，利用窗口子序列Teager能量（WSTE）指标来指示导波信号的局部异常，并设计滑动窗口网络（SWnet）来自动监测缺陷的发生。对道岔钢轨的室外监测表明，该方法可以有效降低温度噪声，并能识别不同温度下转辙钢轨和普通钢轨上横截面变化率（CSCR）分别为 1.16% 和 0.36% 的人为缺陷；此外，SWDTW处理的缺陷信号比尺度变换和DTW处理的缺陷信号表现出更好的缺陷识别性能。
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.