Fallais Dominik Johannes Marius, Henkel Maximilian, Noppe Nymfa, Weijtjens Wout, Devriendt Christof
OWI-Lab, Acoustics and Vibrations Research Group (AVRG), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.
24SEA, Drukpersstraat 4, 1000 Brussels, Belgium.
Sensors (Basel). 2021 Dec 23;22(1):92. doi: 10.3390/s22010092.
Strain measurements using fibre Bragg grating (FBG) optical sensors are becoming ever more commonplace. However, in some cases, these measurements can become corrupted by sudden jumps in the signal, which manifest as spikes or step-like offsets in the data. These jumps are caused by a defect in the FBG itself, which is referred to as peak-splitting. The effects of peak splitting artefacts on FBG strain measurements show similarities with an additive multi-level telegraph noise process, in which the amplitudes and occurrences of the jumps are related to fibre deformation states. Whenever it is not possible to re-assess the raw spectral data with advanced peak tracking software, other means for removing the jumps from the data have to be found. The two methods presented in this article are aimed at removing additive multi-level random telegraph noise (RTN) from the raw data. Both methods are based on denoising the sample wise difference signal using a combination of an outlier detection scheme followed by an outlier replacement step. Once the difference signal has been denoised, the cumulative sum is used to arrive back at a strain time series. Two methods will be demonstrated for reconstructing severely corrupted strain time series; the data for this verification has been collected from sub-soil strain measurements obtained from an operational offshore wind-turbine. The results show that the proposed methods can be used effectively to reconstruct the dynamic content of the corrupted strain time series. It has been illustrated that errors in the outlier replacements accumulate and can cause a quasi-static drift. A representative mean value and drift correction are proposed in terms of an optimization problem, which maximizes the overlap between the reconstruction and a subset of the raw data; whereas a high-pass filter is suggested to remove the quasi static drift if only the dynamic band of the signal is of interest.
使用光纤布拉格光栅（FBG）光学传感器进行应变测量正变得越来越普遍。然而，在某些情况下，这些测量可能会因信号的突然跳变而受到干扰，这些跳变在数据中表现为尖峰或阶梯状偏移。这些跳变是由FBG本身的缺陷引起的，被称为峰值分裂。峰值分裂伪像对FBG应变测量的影响与加性多级电报噪声过程有相似之处，其中跳变的幅度和出现情况与光纤变形状态有关。每当无法使用先进的峰值跟踪软件重新评估原始光谱数据时，就必须找到其他从数据中去除跳变的方法。本文提出的两种方法旨在从原始数据中去除加性多级随机电报噪声（RTN）。这两种方法都基于使用异常值检测方案和异常值替换步骤相结合的方式对逐样本差分信号进行去噪。一旦差分信号被去噪，累积和用于得到应变时间序列。将展示两种用于重建严重受损应变时间序列的方法；此验证数据是从一个运行中的海上风力涡轮机的地下应变测量中收集的。结果表明，所提出的方法可以有效地用于重建受损应变时间序列的动态内容。已经表明，异常值替换中的误差会累积并导致准静态漂移。针对一个优化问题提出了一个代表性的平均值和漂移校正，该优化问题使重建与原始数据子集之间的重叠最大化；而如果只对信号的动态频段感兴趣，则建议使用高通滤波器去除准静态漂移。
