52米风力涡轮机叶片的结构健康监测：疲劳试验期间损伤扩展的检测

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

DataCentric Engineering Pub Date : 2022-06-07 DOI:10.1017/dce.2022.20

M. A. Fremmelev, P. Ladpli, E. Orlowitz, L. Bernhammer, M. McGugan, K. Branner

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引用次数: 5

摘要

本文研究了商用52米风力机叶片疲劳试验过程中的损伤检测问题。以层合裂纹的形式对叶片进行了不同的人为损伤。损伤的长度是人为增加的，在疲劳加载过程中，损伤最终都会扩展并发展成分层。应变计、声发射传感器、分布式加速度计和主动振动监测系统用于跟踪叶片在健康和受损状态下的不同物理响应。基于实测数据，分析了不同传感系统在叶片结构健康监测中的优势和局限性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Structural health monitoring of 52-meter wind turbine blade: Detection of damage propagation during fatigue testing

Abstract This work is concerned with damage detection in a commercial 52-meter wind turbine blade during fatigue testing. Different artificial damages are introduced in the blade in the form of laminate cracks. The lengths of the damages are increased manually, and they all eventually propagate and develop into delaminations during fatigue loading. Strain gauges, acoustic emission sensors, distributed accelerometers, and an active vibration monitoring system are used to track different physical responses in healthy and damaged states of the blade. Based on the recorded data, opportunities and limitations of the different sensing systems for blade structural health monitoring are investigated.

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来源期刊

DataCentric Engineering Engineering-General Engineering

CiteScore

5.60

自引率

0.00%

发文量

审稿时长

12 weeks