Integrating Multiple Algorithms in Autonomous Modal Parameter Estimation

Abstract

Recent work with autonomous modal parameter estimation has shown great promise in the quality of the modal parameter estimation results when compared to results from experienced user interaction using traditional methods. Current research with the Common Statistical Subspace Autonomous Mode Identification (CSSAMI) procedure involves the integration of multiple modal parameter estimation algorithms into the autonomous procedure. The current work uses possible solutions from different traditional methods like Polyreference Time Domain (PTD), Eigensystem Realization Algorithm (ERA) and Polyreference Frequency Domain (PFD) that are combined in the autonomous procedure to yield one consistent set of modal parameter solutions. This final, consistent set of modal parameters is identifiable due to the combination of temporal information (the complex modal frequency) and the spatial information (the modal vectors) in a Z domain state vector of relatively high order (5–10). Since this Z domain state vector has the complex modal frequency and the modal vector as embedded content, sorting consistent estimates from hundreds or thousands of possible solutions is now relatively trivial based upon the use of a state vector involving spatial information.