A novelty mode decomposition method and its application in dynamic characteristic identification of bridge GNSS monitoring data

As the majority of real signals (e.g., the dynamic information obtained during bridge Global Navigation Satellite System (GNSS) monitoring) are nonlinear and nonstationary, variational mode decomposition (VMD) and its derivatives have become popular research topics in recent years worldwide. However, the VMD method has the problem in which improper parameter selection results in a poor decomposition effect. This study analyzes the different decomposition effects of the VMD parameters, such as the decomposition layer number K , the modal frequency bandwidth control parameter α and the noise tolerance τ , and ascertain the insufficiency of the existing optimization methods. On the basis of the investigation and analyses of several decomposition effect evaluation indicators, an adaptive variational modal decomposition algorithm based on multi-objective particle swarm optimization (AVMDbMOPSO) is proposed. The results of our experimental analysis indicate that the method can effectively extract multi-frequencies. Advantages emerge with regard to practical signals collected via the GNSS monitoring of the dynamic characteristics of a bridge. The findings of this research have a positive significance for practical engineering applications.