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

  • Bo Chen
  • Chuanzhi Zang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5666)

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.

Keywords

structural health monitoring unsupervised structure damage classification data clustering artificial immune pattern recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bo Chen
    • 1
  • Chuanzhi Zang
    • 2
    • 3
  1. 1.Department of Mechanical Engineering – Engineering Mechanics/Department of Electrical & Computer EngineeringMichigan Technological UniversityHoughtonUSA
  2. 2.Department of Mechanical Engineering – Engineering MechanicsMichigan Technological UniversityHoughtonUSA
  3. 3.Shenyang Institute of AutomationChinese Academy of ScienceShenyangChina

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