Institute of Materials and Structures, Riga Technical University, Kipsalas iela 6A, LV-1048 Riga, Latvia.
Department of Fundamentals of Machinery Design, Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland.
Sensors (Basel). 2022 May 19;22(10):3867. doi: 10.3390/s22103867.
Optimal sensor placement is one of the important issues in monitoring the condition of structures, which has a major influence on monitoring system performance and cost. Due to this, it is still an open problem to find a compromise between these two parameters. In this study, the problem of optimal sensor placement was investigated for a composite plate with simulated internal damage. To solve this problem, different sensor placement methods with different constraint variants were applied. The advantage of the proposed approach is that information for sensor placement was used only from the structure's healthy state. The results of the calculations according to sensor placement methods were subsets of possible sensor network candidates, which were evaluated using the aggregation of different metrics. The evaluation of selected sensor networks was performed and validated using machine learning techniques and visualized appropriately. Using the proposed approach, it was possible to precisely detect damage based on a limited number of strain sensors and mode shapes taken into consideration, which leads to efficient structural health monitoring with resource savings both in costs and computational time and complexity.
最优传感器布置是结构状态监测的重要问题之一，对监测系统的性能和成本有重大影响。因此，在这两个参数之间找到折衷仍然是一个悬而未决的问题。在这项研究中，针对具有模拟内部损伤的复合材料板，研究了最优传感器布置问题。为了解决这个问题，应用了不同的传感器布置方法，具有不同的约束变体。所提出方法的优点在于，传感器布置的信息仅来自结构的健康状态。根据传感器布置方法的计算结果是可能的传感器网络候选者的子集，这些子集使用不同指标的聚合进行评估。使用机器学习技术对所选传感器网络进行了评估和验证，并进行了适当的可视化。使用所提出的方法，可以根据所考虑的有限数量的应变传感器和模态形状精确地检测损伤，从而实现高效的结构健康监测，在成本以及计算时间和复杂性方面都节省了资源。
