Advances and prospects for optimal sensor placement of structural health monitoring

被引:0
|
作者
Yang, Chen [1 ]
机构
[1] Qian Xuesen Lab of Space Technology, China Academy of Space Technology, Beijing,100094, China
来源
关键词
Efficiency - Structural optimization - Safety engineering - Parameter estimation - Multiobjective optimization - Data acquisition - Reliability analysis - Signal to noise ratio;
D O I
10.13465/j.cnki.jvs.2020.17.012
中图分类号
学科分类号
摘要
Large scale, complex, long life and multi-functional modern engineering structures are exposed to severe service environment for a long time, their structural function and state can deviate from the initial design goal. Using structural health monitoring technology to do health diagnosis and performance evaluation is an important means to ensure structural safety, extend service life and reduce maintenance cost, while sensor system is the primary link of structural health monitoring to directly determine the correctness of structural safety diagnosis. With increasing demands for security and functionality of structures to be monitored, sensor placement is more and more complex, and the high efficiency and high precision data acquisition system of structural state change also poses a higher challenge to the optimization of sensor layout. Here, advances of optimal sensor placement method and evaluation method were reviewed and commented, respectively. Several key problems to be urgently solved including coupling relation between input parameters and performance, reliability analysis, and large scale of sensor placement optimization algorithm's precision, efficiency and evaluation in optimal sensor placement were extracted. Prospects to be developed in future were described including key development directions of optimal sensor placement method with incomplete information and optimal input parameters, multi-type optimal sensor placement method based on the information fusion technology, integrated design for multi-objective optimization sensor placement algorithm, optimal sensor placement algorithm strategy based on artificial intelligence, and adaptive vibration data sampling system under low signal-to-noise ratio and multiple working conditions. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码:82 / 93
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