Abstract

Adaptive learning techniques have recently been considered for structural health monitoring applications due to their flexibility and effectiveness in addressing real-world challenges such as variability in the monitoring of environmental and operating conditions. In this paper, an active learning data selection procedure is proposed that adaptively selects the most informative measurements to include, from multiple available measurements, in estimating structural damage. This is important, since not all the measurements may provide useful information and could reduce performance when processed. Within the adaptive learning framework, the data selection problem is formulated to choose those measurements which are most representative of the diversity within a damage state. This is achieved by extracting time-frequency analysis based statistical similarity features from the measurements, and selecting uniformly distributed subsets to build representative reference sets. The utility of the proposed method is demonstrated by improvements in adaptive learning performance for the estimation of fatigue damage in an aluminum compact tension sample.

Original languageEnglish (US)
Title of host publicationNondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010
DOIs
StatePublished - Jun 17 2010
EventNondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010 - San Diego, CA, United States
Duration: Mar 8 2010Mar 11 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7649
ISSN (Print)0277-786X

Other

OtherNondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010
CountryUnited States
CitySan Diego, CA
Period3/8/103/11/10

Keywords

  • active data selection
  • adaptive learning
  • damage classification
  • discrepancy
  • matching pursuit decomposition
  • optimization
  • statistical similarity
  • structural health monitoring
  • time-frequency analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Chakraborty, D., Kovvali, N., Papandreou-Suppappola, A., & Chattopadhyay, A. (2010). Active learning data selection for adaptive online structural damage estimation. In Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010 [764915] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7649). https://doi.org/10.1117/12.848891