This study aimed to develop a virtual sensing algorithm of structural
vibration for the real-time identification of unmeasured information. First,
certain local point vibration responses (such as displacement and acceleration)
are measured using physical sensors, and the data sets are extended using a
numerical model to cover the unmeasured quantities through the entire spatial
domain in the real-time computation process. A modified time integrator is then
proposed to synchronize the physical sensors and the numerical model using
inverse dynamics. In particular, an efficient inverse force identification
method is derived using implicit time integration. The second-order ordinary
differential formulation and its projection-based reduced-order modeling is
used to avoid two times larger degrees of freedom within the state space form.
Then, the Tikhonov regularization noise-filtering algorithm is employed instead
of Kalman filtering. The performance of the proposed method is investigated on
both numerical and experimental testbeds under sinusoidal and random excitation
loading conditions. In the experimental test, the algorithm is implemented on a
single-board computer, including inverse force identification and unmeasured
response prediction. The results show that the virtual sensing algorithm can
accurately identify unmeasured information, forces, and displacements
throughout the vibration model in real time in a very limited computing
environment.Comment: 24 Pages, 15 Figures, 10 Table