School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Sensors (Basel). 2018 Dec 4;18(12):4264. doi: 10.3390/s18124264.
Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate the impact, extracting numerical signals of the structure, and the wavelet decomposition is used to extract the band energy. Meanwhile, principal component analysis (PCA) is used to reduce the dimensions of the vibration signal. Following this, the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the placement of sensors. Finally, the experimental system is established, and the Product-based Neural Network is used to recognize different impact categories. Three sets of experiments are carried out to verify the optimal results. When three sensors are applied, the average accuracy of the impact recognition is 59.14%; when the number of sensors is four, the average accuracy of impact recognition is 76.95%.
最佳传感器布置是结构健康监测 (SHM) 的一项重要任务。本文设计了一种 SHM 系统，可识别复合材料板中的不同冲击位置和冲击程度。首先，采用有限元方法对冲击进行模拟，提取结构的数值信号，并采用小波分解提取频带能量。同时，采用主成分分析 (PCA) 降低振动信号的维度。然后，采用非支配排序遗传算法 (NSGA-II) 优化传感器的布置。最后，建立实验系统，采用基于产品的神经网络识别不同的冲击类别。进行了三组实验来验证最优结果。当使用三个传感器时，冲击识别的平均准确率为 59.14%；当使用四个传感器时，冲击识别的平均准确率为 76.95%。
