Dynamic Load Identification of Unspecified Metal Structures by Measuring Their Response

Abstract

Many engineering structures are made of metal composite materials. External load information is a key issue for the design and condition monitoring of the structures. Due to the limitation of measurement technology and the external environment, it is difficult to directly measure dynamic loads on structures in many circumstances. This paper focuses on evaluating the external load applied on a structure with unknown dynamic properties. We proposed a novel dynamic load identification method that is based on the Bayesian principle coupled with the extended Kalman filter method. Firstly, the modal parameters are identified under ambient excitation using the Bayesian fast Fourier transform method (FFT). The posterior probability density function (PDF) and covariance of the modal parameters are obtained by the Fourier transform of the response data, and then the modal parameters of the structure are obtained based on unconstrained optimization. Next, the extended Kalman filter method in the modal space is used to update the modal parameters and identify the time-domain information of dynamic loads. The accuracy of the proposed theory was evaluated experimentally using a Bernoulli−Euler beam. The results showed that the method is feasible and efficient.