Structural health monitoring by probability density function of autoregressive-based damage features and fast distance correlation method

Abstract

In this article, the autoregressive time series analysis is used to extract reliable features from vibration measurements of civil structures for damage diagnosis. To guarantee the adequacy and applicability of the time series model, Leybourne–McCabe hypothesis test is used. Subsequently, the probability density functions of the autoregressive model parameters and residuals are obtained with the aid of a kernel density estimator. The probability density function sets are considered as damage-sensitive features of the structure and fast distance correlation method is used to make decision for detecting damages in the structure. Experimental data of a well-known three-story laboratory frame and a large-scale bridge benchmark structure are used to verify the efficiency and accuracy of the proposed method. Results indicate the capability of the method to identify the location and severity of damages, even under the simulated operational and environmental variability.