Abstract

A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data is available. We present an adaptive learning methodology where stochastic models continuously evolve with the time-varying environment and Dirichlet process mixture models are utilized to self-adapt to structure within the data. Coupled with appropriate physics-based phenomenology, the approach provides an adaptive and effective framework for online SHM. The proposed technique is demonstrated for the detection of progressive fatigue damage in a metallic structure under variable-amplitude loading.

Original languageEnglish (US)
Title of host publicationConference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages1678-1682
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 1 2009Nov 4 2009

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other43rd Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/1/0911/4/09

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Chakraborty, D., Kovvali, N., Zhang, J. J., Papandreou-Suppappola, A., & Chattopadhyay, A. (2009). Adaptive learning for damage classification in structural health monitoring. In Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers (pp. 1678-1682). [5469782] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2009.5469782