Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Piotr Omenzetter; Oliver R. de Lautour

doi:10.1117/12.852573

31 March 2010 Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Piotr Omenzetter, Oliver R. de Lautour

Author Affiliations +

Proceedings Volume 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010; 76474S (2010) https://doi.org/10.1117/12.852573
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2010, San Diego, California, United States

Abstract

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

Citation Download Citation

Piotr Omenzetter and Oliver R. de Lautour "Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques", Proc. SPIE 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, 76474S (31 March 2010); https://doi.org/10.1117/12.852573

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