Advances in Acoustic Emission Technology - Proceedings of the World Conference on Acoustic Emission-2013
Advances in Acoustic Emission Technology - Proceedings of the World Conference on Acoustic Emission-2013
复制标题
声发射技术的进展 - 2013 年世界声发射会议论文集
DOI：
10.1007/978-1-4939-1239-1_50
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Bouzid O
中科院分区：
文献类型：
--
作者：
Bouzid O
声无线技术在结构健康监测（SHM）应用中的集成由于对高采样率、附加通信带宽、存储空间和功率资源的要求而引入了新的挑战。为了规避这些挑战，本章提出了一种新的解决方案，通过建立一个无线SHM技术结合声发射（AE）与现场部署的风力涡轮机的结构。该解决方案需要低于奈奎斯特速率的低采样速率。此外，从混叠的AE信号中提取的特征，而不是重建的原始信号上的无线节点被用来监测AE事件，如风，雨，强冰雹，鸟击在不同的环境条件下与人工AE源。时间特征提取算法，除了主成分分析（PCA）的方法，用于提取和分类的相关信息，这反过来又被用来分类或识别的测试条件所代表的响应信号。这种新的技术在风力涡轮机叶片的监测过程中产生了显着的数据减少。
Integration of acoustic wireless technology in structural health monitoring (SHM) applications introduces new challenges due to requirements of high sampling rates, additional communication bandwidth, memory space, and power resources. In order to circumvent these challenges, this chapter proposes a novel solution through building a wireless SHM technique in conjunction with acoustic emission (AE) with field deployment on the structure of a wind turbine. This solution requires a low sampling rate which is lower than the Nyquist rate. In addition, features extracted from aliased AE signals instead of reconstructing the original signals on-board the wireless nodes are exploited to monitor AE events, such as wind, rain, strong hail, and bird strike in different environmental conditions in conjunction with artificial AE sources. Time feature extraction algorithm, in addition to the principal component analysis (PCA) method, is used to extract and classify the relevant information, which in turn is used to classify or recognise a testing condition that is represented by the response signals. This proposed novel technique yields a significant data reduction during the monitoring process of wind turbine blades.