Structural health monitoring using statistical process control

Abstract

This paper poses the process of structural health monitoring in the context of a statistical pattern recognition paradigm. This paper particularly focuses on applying a statistical process control (SPC) technique known as an "X-bar control chart" to vibration-based damage diagnosis. A control chart provides a statistical framework for monitoring future measurements and for identifying new data that are inconsistent with past data. First. an autoregressive (AR) model is fit to the measured time histories from an undamaged structure. Coefficients of the AR model are selected as the damage-sensitive features for the subsequent control chart analysis. Next, control limits of the X-bar control chart are constructed based on the features obtained from the initial structure. Finally, the AR coefficients of the models fit to subsequent new data are monitored relative to the control limits. A statistically significant number of features outside the control limits indicate a system transition from a healthy state to a damage state. A unique aspect of this study is the coupling of various projection techniques such as principal component analysis and linear and quadratic discriminant operators with the SPC in an effort to enhance the discrimination between features from the undamaged and damaged structures. This combined statistical procedure is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged. The coupled approach captures a clearer distinction between undamaged and damaged vibration responses than by applying an SPC alone.