We propose a sequential Monte Carlo (SMC) based progressive structural damage diagnosis framework that tracks damage by integrating information from physics-based damage evolution models and using stochastic relationships between the measurements and the damage. The approach described in this paper adaptively configures the sensors used to collect the measurements using the minimum predicted mean squared error (MSE) as the performance metric. Optimization is performed globally over the entire search space of all available sensors. Results are presented for the diagnosis of fatigue damage in a notched laminate, demonstrating the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationHealth Monitoring of Structural and Biological Systems 2010
EditionPART 1
StatePublished - 2010
EventHealth Monitoring of Structural and Biological Systems 2010 - San Diego, CA, United States
Duration: Mar 8 2010Mar 11 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
NumberPART 1
ISSN (Print)0277-786X


OtherHealth Monitoring of Structural and Biological Systems 2010
Country/TerritoryUnited States
CitySan Diego, CA


  • Hidden Markov model
  • Particle filter
  • Progressive damage diagnosis
  • Sensor optimization
  • Sequential Monte Carlo
  • Structural health monitoring

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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