Abstract The development of a viable structural health monitoring (SHM) technology for the operational condition monitoring of wind turbine blades is of great interest to the wind industry. In order for any SHM technology to achieve the technical readiness and performance required for an operational implementation, advanced signal processing algorithms need to be developed to adaptively remove noise and retain the underlying signals of interest that describe the damage-related information. The wavelet packet transform decomposes a measured time domain signal into a time-frequency representation enabling the removal of noise that may overlap with the signal of interest in time and/or frequency. However, the traditional technique suffers from several assumptions limiting its applicability in an operational SHM environment, where the noise conditions commonly exhibit erratic behavior. Furthermore, an exhaustive number of options exist when selecting the parameters used in the technique with limited guidelines that can help select the most appropriate options for a given application. Appropriately defining the technique tends to be a daunting task resulting in a general avoidance of the approach in the field of SHM. This work outlines an adaptive wavelet packet denoising algorithm applicable to numerous SHM technologies including acoustics, vibrations, and acoustic emission. The algorithm incorporates a blend of non-traditional approaches for noise estimation, threshold selection, and threshold application to augment the denoising performance of real-time structural health monitoring measurements. Appropriate wavelet packet parameters are selected through a simulation considering the trade-off between signal to noise ratio improvement and amount of signal energy retained. The wavelet parameter simulation can be easily replicated to accommodate any SHM technology where the underlying signal of interest is known, as is the case in most active-based approaches including acoustic and wave-propagation techniques. The finalized adaptive wavelet packet algorithm is applied to a comprehensive dataset demonstrating an active acoustic damage detection approach on a ~46 m wind turbine blade. The quality of the measured data and the damage detection performance obtained from simple spectral filtering is compared with the proposed wavelet packet technique. It is shown that the damage detection performance is enhanced in all but one test case by as much as 60%, and the false detection rate is reduced. The approach and the subsequent results presented in this paper are expected to help enable advancement in the performance of several established SHM technologies and identifies the considered acoustics-based SHM approach as a noteworthy option for wind turbine blade structural health monitoring.

中文翻译：

---

一种用于增强风力涡轮机叶片主动声损伤检测的自适应小波包去噪算法

摘要 风力涡轮机叶片运行状态监测的可行结构健康监测（SHM）技术的发展引起了风电行业的极大兴趣。为了使任何 SHM 技术达到操作实施所需的技术准备和性能，需要开发先进的信号处理算法来自适应地去除噪声并保留描述损坏相关信息的潜在感兴趣信号。小波包变换将测量的时域信号分解为时频表示，从而能够去除可能在时间和/或频率上与感兴趣的信号重叠的噪声。然而，传统技术受到几个限制其在可操作 SHM 环境中的适用性的假设的影响，其中噪声条件通常表现出不稳定的行为。此外，在选择技术中使用的参数时，存在大量选项，但指南有限，可以帮助为给定应用选择最合适的选项。适当地定义该技术往往是一项艰巨的任务，导致在 SHM 领域普遍避免使用该方法。这项工作概述了适用于包括声学、振动和声发射在内的众多 SHM 技术的自适应小波包去噪算法。该算法融合了噪声估计、阈值选择和阈值应用的非传统方法，以增强实时结构健康监测测量的去噪性能。考虑到信噪比改进和保留的信号能量量之间的权衡，通过模拟选择合适的小波包参数。小波参数模拟可以很容易地复制以适应任何已知感兴趣的潜在信号的 SHM 技术，就像大多数基于有源方法的情况一样，包括声学和波传播技术。最终的自适应小波包算法应用于综合数据集，展示了在 ~46 m 风力涡轮机叶片上的主动声学损伤检测方法。测量数据的质量和从简单频谱滤波获得的损伤检测性能与所提出的小波包技术进行了比较。结果表明，除了一个测试用例外，其他所有测试用例的损坏检测性能都提高了 60%，并降低了误检率。本文中介绍的方法和随后的结果有望帮助提高几种已建立的 SHM 技术的性能，并将所考虑的基于声学的 SHM 方法确定为风力涡轮机叶片结构健康监测的一个值得注意的选项。