Abstract Summary
Operational modal analysis is a technique developed to estimate the modal parameters of a given structural or mechanical system using vibration data recorded with sensors. There are many approaches to the problem: parametric and non-parametric; time domain and frequency domain; maximum likelihood and least squares; Bayesian and non-Bayesian, … This work presents the estimation of modal parameters using Bayesian estimation in time domain. Bayesian estimation is based in three steps: 1. Prior distributions: is the distribution of the modal parameters before the vibration data is recorded; 2. Likelihood function: is the distribution of the recorded data conditional on the modal parameters; 3. Posterior distributions: is the distribution of the modal parameters taking into account the recorded data. The objective in Bayesian estimation is to find the posterior distributions of the parameters. In this work, the posterior distributions of modal parameters are computed using the following assumptions: 1. Normal distributions are used for prior distributions. 2. The likelihood function is derived using the state space model and the Kalman filter; 3. Posterior distributions are computed using Markov Chain Montecarlo sampling by mean of the state-of-the-art software Stan. The objective of the paper is to analyze the performance of this approach in Operational Modal Analysis.
