In smart structures, achieving a reliable set of measurement signals to monitor the system’s performance is critical. Also, the essence of using the optimum batch of sensors and an efficient algorithm to process these signals is significant for active vibration control of these structures. This paper primarily introduces a method of sensor fusion using the Kalman filter as an observer to gain the proper position signal from both an accelerometer and an ultrasonic sensor mounted on the tip of a cantilever beam. The main goal of this procedure is to eliminate both sensors’ shortcomings. Also, we present a novel approach to estimate the overall shape of the beam, using only the tip position signal. To this end, a high-speed camera is used to capture the motion of three markers on the beam under different excitation frequencies. Then, three long short-term memory networks are trained by deep learning methods, using a finite sequence of beam tip position, to act as observers for estimating the shape of the beam. The proposed methods are simulated and then validated by experiments.

在智能结构中，获得一组可靠的测量信号来监控系统性能至关重要。此外，使用最佳批次传感器和有效算法来处理这些信号的本质对于这些结构的主动振动控制非常重要。本文主要介绍了一种传感器融合方法，使用卡尔曼滤波器作为观察器，从安装在悬臂梁尖端的加速度计和超声波传感器获得正确的位置信号。此过程的主要目标是消除两个传感器的缺点。此外，我们提出了一种仅使用尖端位置信号来估计光束整体形状的新方法。为此，使用高速相机捕捉不同激发频率下光束上三个标记的运动。然后，三个长短期记忆网络通过深度学习方法训练，使用光束尖端位置的有限序列，作为估计光束形状的观察者。所提出的方法被模拟，然后通过实验验证。