A single-mode recursive validation method for modal identification of linear time-varying structures based on prior knowledge

Discerning the spurious modes for time-invariant or extremely slow time-varying structures with time-consuming tools (stabilization diagram and clustering), which is the main task of the automated modal identification, has been developed in last years. However, there is still a challenge the recursive identification of the linear time-varying structures. This study presents a single-mode recursive validation method for the recursive identification of the linear time-varying structures. Since one major issue is that the properties of the physical modes are time-varying, the proposed method extracts the information about the current state of the time-varying structures from the nonstationary vibration responses via deep learning method for the separation between physical and spurious modes. For the sake of effective elimination to spurious modes and control of computation efforts, the proposed method utilizes the application-dependent prior knowledge rather than iterations or high-dimensional optimizations, with robustness to hyperparameters. It can be combined with any parametric system identification method. A time-varying stiffness numerical example and a time-varying mass distribution experimental example illustrate the performance of the proposed modal validation method under various time-varying processes.