Continuous structural monitoring using statistical process control

A damage detection problem is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as `control charts' to vibration-based damage detection. First, an auto-regressive (AR) model is fitted to the measured time histories from an undamaged structure. Residual errors, which quantify the difference between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features. Next, the average and variability of the selected features are monitored by the X-bar and S control charts. A statistically significant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state. For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged.