Unsupervised Structure Damage Classification Based on the Data Clustering and Artificial Immune Pattern Recognition

Authors:
Bo Chen;Chuanzhi Zang
Affiliations:
Department of Mechanical Engineering --- Engineering Mechanics/Department of Electrical & Computer Engineering, Michigan Technological University, Houghton, USA;Department of Mechanical Engineering --- Engineering Mechanics, Michigan Technological University, Houghton, USA and Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
Venue:
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Year:
2009

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Abstract

This paper presents an unsupervised structure damage classification algorithm based on the data clustering technique and the artificial immune pattern recognition. The presented method uses time series measurement of a structure's dynamic response to extract damage-sensitive features for the structure damage classification. The Data Clustering (DC) technique is employed to cluster training data to a specified number of clusters and generate the initial memory cell set. The Artificial Immune Pattern Recognition (AIPR) algorithms are integrated with the data clustering algorithms to provide a mechanism for the evolution of memory cells. The combined DC-AIPR method has been tested using a benchmark structure. The test results show the feasibility of using the DC-AIPR method for the unsupervised structure damage classification.