Time Series Based Structural Damage Detection Algorithm Using Gaussian Mixtures Modeling

Abstract

In this paper, a time series based detection algorithm is proposed utilizing the Gaussian Mixture Models. The two critical aspects of damage diagnosis that are investigated are detection and extent. The vibration signals obtained from the structure are modeled as autoregressive moving average (ARMA) processes. The feature vector used consists of the first three autoregressive coefficients obtained from the modeling of the vibration signals. Damage is detected by observing a migration of the extracted AR coefficients with damage. A Gaussian Mixture Model (GMM) is used to model the feature vector. Damage is detected using the gap statistic, which ascertains the optimal number of mixtures in a particular dataset. The Mahalanobis distance between the mixture in question and the baseline (undamaged) mixture is a good indicator of damage extent. Application cases from the ASCE Benchmark Structure simulated data have been used to test the efficacy of the algorithm. This approach provides a useful framework for data fusion, where different measurements such as strains, temperature, and humidity could be used for a more robust damage decision.