Development of a Modal Selection Method for Full Strain Field Estimation

Abstract

Abstract Full field response of a structure is critical for evaluating the performance of large slender structures. Since only several discrete measurements can be acquired during operation, the data expansion method is important for the estimation of the full field responses of the large complex structure. In previous studies, modal transformation methods were mainly applied in model reduction/expansion and global shape sensing. Compared to other expansion methods, the modal method is straightforward to implement and computational efficient, which makes it the most suitable approach for real-time expansion. However, only the first several modes were included in the modal transformation method in previous studies. Since the errors due to truncated mode components can occur under high frequency band excitations, it is necessary to include all of the modes that contribute significantly to the responses of the structure. Therefore, in this study, a modal selection method based on operational modal analysis (OMA) is proposed for selecting proper modes. The modal characteristics of the system were derived with the strain data at several discrete locations. The contribution of each mode was quantified. By sorting the modes based on their contribution, the most significant modes can be used in the expansion process. Two operational modal analysis methods, stochastic system identification (SSI) and frequency domain decomposition (FDD), were considered and compared. The proposed approach was implemented with a computational model. Considerable improvement has been observed when high bandwidth excitations were added. The proposed modal selection method can successfully rank the participated modes. It can improve the accuracy of the modal transformation approach as shown in the impact loading case. It can be used for data expansion even when high frequency band is excited. Finally, we believe the novel methods presented in this study could be used in the development of more reliable health monitoring systems for turbomachinery.