基于光纤光栅传感器和卡尔曼滤波器的载荷识别算法

An Algorithm of Dynamic Load Identification Based on FBG Sensor and Kalman Filter

载荷识别在结构健康监测中的地位十分重要,为了在结构健康监测的同时利用最优化算法对系统进行有效控制,提出了一种基于光纤光栅(FBG)传感器和卡尔曼滤波器的载荷识别算法。该算法建立在卡尔曼滤波器的基础上,以FBG传感器测得的应变值作为观测信号,通过卡尔曼滤波器产生的增益矩阵、新息序列和协方差矩阵,利用最小二乘算法实时估计载荷的大小。此算法只需采集前一时刻的估计值和当前时刻的观测值即可估计出当前时刻的载荷,无需存储和读取大量数据。同时,基于卡尔曼滤波器在进行结构健康监测的同时能够应用最优化算法对系统进行控制。为了对识别算法进行验证,采用梁系统作为仿真和试验对象,通过FBG传感器测得的应变值识别载荷。结果表明,所提动态载荷识别算法能够很好地抑制噪声,具有良好的稳定性和实时性。

Load identification plays an important role in structural health monitoring. A method to identify load for a cantilever beam based on dynamic strain measurement by FBG （fiber Bragg grating） sensors is presented to facilitate the control over the system during structural health monitoring. The algorithm is based on Kalman filter, using the strain measured by FBG sensors as observed signal, and the gain matrix, the residual innovation sequences and covariance matrix generated by Kalman filter to estimate the load in real time through least squares algorithm. The proposed load identification method based on FBG sensors is a recursive method, which means that recent measurement value and previous estimated value need to be kept in storage. This will save considerable memory and greatly decreases the system burden. The proposed method is based on Kalman filter, and this can be helpful for system control by using optimal control theory after identifying 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 good stability and real-time capability.