摘要
结构健康监测系统中光纤光栅应变计长期工作易发生故障,其本身的状态诊断与维护关系到系统的可靠性。基于同结构监测中光纤光栅应变计原始输出具有相同规律的特点,提出一种传感器异常诊断方法,利用信号处理方法提取测点样本长度、标准差、能量值及主成分周期值作为特征。通过循环迭代获取特征值聚合中心点,将特征偏离中心点距离标准化后融合为综合异常指数,实现传感器的异常和故障判别。仿真证明,故障测点为总测点数目的20%以内,可有效识别故障测点。对500米口径球面射电望远镜(FAST)工程健康监测系统的416个光纤光栅应变计测点进行诊断,成功提取317个传感器的特征,识别出4个故障测点与14个异常测点。该方法不需要先验知识训练,基于数据本身特性实现诊断,对光纤光栅应变计的多种故障有较强的指示作用,满足FAST结构健康监测的工程需求。
Fiber Bragg grating strain gauges are usually used to access the structural strain and monitor the health of the structure.However,the performance of the structural health monitoring system is dependent on the status of such a large number of sensors,which are quite easy to break down after a long-term service in various conditions.Based on the phenomenon that similar characteristics will be observed for the outpout of the fiber grating strain gauge in the same structure,we propose a novel Malfunction-diagnosis method for the sensors in the fiber Bragg grating strain gauges.The proposed method extracted the eigenvalue s(i.e.,sample data length,standard deviation,energy value and principal component period)using signal processing algorithm.The eigenvalue convergence center points were determined by loop iterations.The eigenvalue distances from the convergence centers were standardized and merged into a comprehensive index for the health monitoring of the sensors.The simulation proves that the malfunction points can be effectively identified when the number of malfunction points is below 20%of the total points.The proposed method was used to monitor the 416 fiber Bragg grating strain gauges of five-hundred-meter aperture spherical telescope(FAST)health monitoring system,the results showed that the eigenvalue lists of 317 sensors could be extracted and 4 malfunction points and 14 abnormal points could be identified.Instead of a large amount of training with prior knowledge,the proposed method could provide reliable malfunction diagnosis for the sensors in fiber Bragg grating strain gauges just based on the characteristics of the data,which might be practical useful for the health monitoring of the FAST structure.
作者
孙晓
王清梅
李振伟
楚敬敬
SUN Xiao;WANG Qing-mei;LI Zhen-wei;CHU Jing-jing(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China;National Astronomical Observatories,Chinese Academy of Science,Beijing 100012,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2021年第11期2581-2589,共9页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.11803053)。
