Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2022 Jul 12;22(14):5203. doi: 10.3390/s22145203.
The inverse finite element method (iFEM) is one of the most effective deformation reconstruction techniques for shape sensing, which is widely applied in structural health monitoring. The distribution of strain sensors affects the reconstruction accuracy of the structure in iFEM. This paper proposes a method to optimize the layout of sensors rationally. Firstly, this paper constructs a dual-objective model based on the accuracy and robustness indexes. Then, an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm is developed for this model, which introduces initialization strategy, the adaptive inertia weight strategy, the guided particle selection strategy and the external candidate solution (ECS) set maintenance strategy to multi-objective particle swarm optimization (MOPSO). Afterwards, the performance of IAMOPSO is verified by comparing with MOPSO applied on the existing inverse beam model. Finally, the IAMOPSO is employed to the deformation reconstruction of complex plate-beam model. The numerical and experimental results demonstrate that the IAMOPSO is an excellent tool for sensor layout in iFEM.
逆有限元法（iFEM）是形状传感中最有效的变形重构技术之一，广泛应用于结构健康监测。应变传感器的分布会影响 iFEM 中结构的重构精度。本文提出了一种合理优化传感器布局的方法。首先，本文基于精度和稳健性指标构建了一个双目标模型。然后，针对该模型开发了一种改进的自适应多目标粒子群优化（IAMOPSO）算法，该算法引入了初始化策略、自适应惯性权重策略、引导粒子选择策略和外部候选解集（ECS）维护策略，对多目标粒子群优化（MOPSO）进行了改进。随后，通过将 IAMOPSO 与应用于现有逆梁模型的 MOPSO 进行比较，验证了 IAMOPSO 的性能。最后，将 IAMOPSO 应用于复杂板梁模型的变形重构。数值和实验结果表明，IAMOPSO 是 iFEM 中传感器布局的优秀工具。
