# Uncertainty-Based Fusion Method for Structural Modal Parameter Identification

## TL;DR

This paper introduces a new method to combine time and frequency domain techniques for identifying structural modal parameters, improving accuracy and reducing errors.

## Contribution

The novel contribution is an uncertainty-based fusion method that unifies AR and LMF models for better modal parameter estimation.

## Key findings

- The fusion method reliably estimates modal parameters and reduces large estimation errors.
- It enhances identification capabilities by reducing the chance of missing structural modes.
- Both numerical and experimental examples validate the method's effectiveness.

## Abstract

The structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement noise. In this work, an uncertainty-based fusion method for structural mode identification is proposed to merge the advantages of different methods. The extensively applied time-domain AutoRegressive (AR) and frequency-domain Left-Matrix Fraction (LMF) models are expressed in a unified parametric model. With this unified model, a generalized framework is developed to identify the modal parameters of structures and compute variances associated with modal parameter estimates. The final modal parameter estimates are computed as the inverse-variance weighted sum of the results identified from different methods. A numerical and an experimental example demonstrate that the proposed method can obtain reliable modal parameter estimates, substantially mitigating the occurrence of extremely large estimation errors. Furthermore, the fusion method demonstrates enhanced identification capabilities, effectively reducing the likelihood of missing structural modes.