Dynamic load identification is an inverse problem concerned with finding the load applied on a structure when the dynamic characteristics and the response of the structure are known. In engineering applications, some of the structure parameters such as the mass or the stiffness may be unknown and/or may change in time. In this paper, an online dynamic load identification algorithm based on an extended Kalman filter is proposed. The algorithm not only identifies the load by measuring the structural response but also identifies the unknown structure parameters and tracks their changes. We discuss the proposed algorithm for the cases when the unknown parameters are the stiffness or the mass coefficients. Furthermore, for a system with many degrees of freedom and to achieve online computations, we implement the model reduction theory. Thus, we reduce the number of degrees of freedom in the resulting symmetric system before applying the proposed extended Kalman filter algorithm. The algorithm is used to recover the dynamic loads in three numerical examples. It is also used to identify the dynamic load in a lab experiment for a structure with varying parameters. The simulations and the experimental results show that the proposed algorithm is effective and can simultaneously identify the parameters and any changes in them as well as the applied dynamic load.

In order to ensure the reliability of the structural design, it is necessary to know the external loads acting on the structure. In this paper, we propose a novel method to identify the dynamic loads based on function principles in the time domain. Assuming the external load remains constant within one micro segment, we establish a linear relationship between external load and structural response in the micro segments based on the mechanical energy conservation law. Next, the external load is obtained by solving the inverse problem in each micro-segment. Finally, the external load in the whole time domain is achieved by fitting the load-identification results in each micro segment. In order to verify the effectiveness and accuracy, single-force and two-force identification, and load identification with noise simulations, are performed on the structures, and the identification results are compared to the ones of the traditional time-domain method with a deviation of less than 5%. The proposed method can effectively solve the problem of cumulative errors in the time-domain method, while its resistance to noise interference is also strong. At last, we verify the experimental performance of the proposed method. The experimental results show the effectiveness and high accuracy of the proposed method. This work presents a first attempt to solve the structural dynamic load with an approach based on a function principle.

This paper proposes a novel dynamic response reconstruction method based on the Kalman filter which can simultaneously identifies external excitation and reconstructs dynamic responses at unmeasured positions. The weighted least squares method determines the load weighting matrix for excitation identification, while minimum variance unbiased estimation determines the Kalman filter gain. The excitation prediction Kalman filter is constructed through time, excitation, and measurement updates. Subsequently, the response at the target point is reconstructed using the state vector, observation matrix, and excitation influence matrix obtained through the excitation prediction Kalman filter algorithm. An algorithm for reconstructing responses in Continuous System using the excitation prediction Kalman filtering algorithm in modal space is derived. The proposed structural dynamic response reconstruction method evaluates response reconstruction and load identification performance under various load types and errors through simulation examples. Finally, a test system for structural dynamic response reconstruction on simply supported beams is constructed and tested. Results demonstrate accurate excitation identification under different load conditions and simultaneous reconstruction of target point responses, verifying the feasibility and reliability of the method.