Adaptive learning for damage classification in structural health monitoring

  • Authors:

  • D. Chakraborty;N. Kovvali;J. J. Zhang;A. Papandreou-Suppappola;A. Chattopadhyay

  • Affiliations:

  • School of Electrical, Computer and Energy Engineering, Arizona State University;School of Electrical, Computer and Energy Engineering, Arizona State University;School of Electrical, Computer and Energy Engineering, Arizona State University;School of Electrical, Computer and Energy Engineering, Arizona State University;School of Mechanical, Aerospace, Chemical and Materials Engineering, Arizona State University

  • Venue:
  • Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
  • Year:
  • 2009

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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 physicsbased 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.