Song Xuegang, Zhang Yuexin, Liang Dakai
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 210000 Nanjing, China.
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, 210000 Nanjing, China.
Sensors (Basel). 2017 Jul 28;17(8):1733. doi: 10.3390/s17081733.
Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance.
载荷识别在结构健康监测中起着重要作用，其目的是防止结构失效。为了识别线性系统和非线性系统的载荷，本文提出了基于光纤布拉格光栅（FBG）传感器动态应变测量的悬臂梁载荷识别方法。对于线性系统，所提出的逆方法由无载荷项的卡尔曼滤波器和线性估计器组成。对于非线性系统，所提出的逆方法由无载荷项的容积卡尔曼滤波器（CKF）和非线性估计器组成。在载荷识别过程中，采用有限元法（FEM）构建梁结构的状态方程。使用卡尔曼滤波器或CKF抑制噪声。由卡尔曼滤波器或CKF生成的残差创新序列、增益矩阵和创新协方差用于识别载荷。为了证明所提方法的有效性，对梁结构进行了数值模拟和实验验证，结果表明该方法具有优异的性能。
