Nearest neighbor and learning vector quantization classification for damage detection using time series analysis

The application of time series analysis methods to structural health monitoring (SHM) is a relatively new but promising approach. This study focuses on the use of statistical pattern recognition techniques to classify damage based on analysis of the time series model coefficients. Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures; a three-storey laboratory bookshelf structure and the ASCE Phase II experimental SHM benchmark structure in undamaged and various damaged states. The coefficients of the AR models were used as damage sensitive features. Principal component analysis and Sammon mapping were used to firstly obtain two-dimensional projections for quick visualization of clusters among the AR coefficients corresponding to the various damage states, and later for dimensionality reduction of data for automatic damage classifications. Data reduction based on the selection of sensors and AR coefficients was also studied. Two supervised learning algorithms, nearest neighbor classification and learning vector quantization were applied in order to systematically classify damage into states. The results showed both classification techniques were able to successfully classify damage.