Abnormal diagnosis of fiber Bragg grating strain gauges in health monitoring system
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Abnormal diagnosis of fiber Bragg grating strain gauges in health monitoring system
Optics and Precision EngineeringPages: 1-9(2021)
作者机构:
1.青岛科技大学 自动化与电子工程学院,山东 青岛 266061
2.中科院国家天文台,北京 100012
作者简介:
基金信息:
DOI:
CLC:TP394.1;TH691.9
Received:15 March 2021,
Revised:11 May 2021,
稿件说明:
移动端阅览
孙晓,王清梅,李振伟等.结构健康监测系统光纤Bragg光栅应变计异常诊断[J].光学精密工程,
SUN Xiao,WANG Qing-mei,LI Zhen-wei,et al.Abnormal diagnosis of fiber Bragg grating strain gauges in health monitoring system[J].Optics and Precision Engineering,
SUN Xiao,WANG Qing-mei,LI Zhen-wei,et al.Abnormal diagnosis of fiber Bragg grating strain gauges in health monitoring system[J].Optics and Precision Engineering,DOI:10.37188/OPE..0001
Abnormal diagnosis of fiber Bragg grating strain gauges in health monitoring system
In structural health monitoring systems, fiber Bragg grating strain gauges are generally used for structural strain monitoring. The large number of sensors’ long-term operation could lead to malfunction, of whose status diagnosis and maintenance are related to the reliability of the system. Considering the original output of the fiber grating strain gauge in the same structure monitoring has the same characteristic, the paper proposes a novel sensor abnormal diagnosis scheme using signal processing methods, while the sample data length, standard deviation, energy value and principal component period value are processed to observe features. The feature aggregation center point is observed through loop iteration, afterwards the distance from the center point of the feature is standardized and merged into a comprehensive anomaly index to identify abnormalities and faults of sensors. The simulation proves that the fault measurement points can be effectively identified while the number of fault measurement points is less than 20% of the total. By diagnosing 416 fiber Bragg grating strain gauges of health monitoring system of FAST project, the feature list of 317 sensors is successfully extracted, and 4 faulty measuring points and 14 abnormal measuring points are identified. If there are too many fault points, the data will be too discrete. The method realizes diagnosis based on the characteristics of the data and does not require prior knowledge training. It has a strong indication effect on a variety of faults, and the diagnosis result is accurate and meets practical needs.
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references
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