Beam deflection estimation by Monte Carlo simulation and Kalman filter based ultrasonic distance sensor. Issue 2 (November 2020) - Batbooti, Raed S.; Mohammed, Bassam A.; Jabbar, Tahseen Ali
Abstract: The beam deflection estimation is of primary importance on design stage and health monitoring stage as it provide an indication about the failure of the structure. The bending theory based model is a deterministic model that widely used to determine the beam deflection theoretically. Two significant factors are influenced the beam deflection estimation in bending theory, the applied load and cross section dimensions. Taking the uncertainty of these two factors into account provide an opportunity to understand the uncertainty of the beam deflection estimation. In this paper the so called Monte Carlo Simulation (MCS) is used to create stochastic bending model from bending theory model by generating random load and random cross section dimensions based on the accuracy of measurement devices. On the other hand, the beam deflection can be estimated experimentally within a specific accuracy depending on the accuracy of measuring device. A validation tool is needed to combine the experimental and theoretical results in one model to obtain a more accurate estimator than using experimental results alone or theoretical deterministic model alone. In the present work the Kalman filter (KF) is used to put together the experimental results from a cheap distance sensor (ultrasonic distance sensor) with bending theory results to estimate the deflection of simply supported beam. The KF algorithm taking into account the uncertainty of process and the uncertainty of the sensor, resulted in robust estimation of beam deflection.