话筒
麦克风阵列
涡轮机
涡轮叶片
声学
参数统计
结构健康监测
计算机科学
鉴定(生物学)
可靠性(半导体)
风力发电
比例(比率)
海洋工程
工程类
结构工程
扬声器
航空航天工程
功率(物理)
数学
物理
统计
植物
电气工程
量子力学
生物
作者
Shilin Sun,Tianyang Wang,Hongxing Yang,Fulei Chu
标识
DOI:10.1177/14759217221085655
摘要
Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and efficiency of wind energy generation, and it is a challenging issue due to the complicated structures and variational operating conditions. In this investigation, a SHM method for wind turbine blades based on the microphone array and acoustic source identification is proposed. With the equipment of loudspeakers in blade cavities, damage-related information is excited to be captured by the array. To generate accurate acoustic maps with high spatial resolutions, a novel algorithm for sparsity-based sound field reconstruction is developed based on the generalized minimax-concave penalty function. With a laboratory-scale wind turbine model, damage identification performance of the proposed method is evaluated under different parametric and measuring conditions, and experiments are conducted under diverse blade health conditions. Results reveal that and both internal and external damage in operating blades can be recognized as acoustic sources, and satisfactory performance of the proposed method can be guaranteed with appropriate parameters. Furthermore, determination criteria for parameters are concluded with respect to the variation of measuring conditions. This prototype study provides useful insights into the development of effective SHM systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI