Abstract

A novel approach is proposed for effectively estimating evolving damage state parameters using sensor data collected in real-time. The approach uses a state-space setting to formulate the progressive damage estimation in a Bayesian framework. The formulation consists of a damage evolution process (assumed Markov and obtained from fracture mechanics) and a measurement-damage relationship (based on hidden Markov modeling with joint time-frequency features). The damage state estimation is performed efficiently using sequential Monte Carlo techniques. In order to demonstrate the utility of the proposed method, results are presented for fatigue crack estimation for an aluminum CT sample subjected to variable-amplitude loading.

Original languageEnglish (US)
Title of host publicationStructural Health Monitoring 2009: From System Integration to Autonomous Systems - Proceedings of the 7th International Workshop on Structural Health Monitoring, IWSHM 2009
PublisherDEStech Publications
Pages569-576
Number of pages8
Volume1
ISBN (Print)9781605950075
Publication statusPublished - 2009
Event7th International Workshop on Structural Health Monitoring: From System Integration to Autonomous Systems, IWSHM 2009 - Stanford, United States
Duration: Sep 9 2009Sep 11 2009

Other

Other7th International Workshop on Structural Health Monitoring: From System Integration to Autonomous Systems, IWSHM 2009
CountryUnited States
CityStanford
Period9/9/099/11/09

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ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications

Cite this

Zhou, W., Kowali, N., Papandreou-Suppappola, A., Peralta, P., & Chattopadhyay, A. (2009). Progressive damage estimation using sequential Monte Carlo techniques. In Structural Health Monitoring 2009: From System Integration to Autonomous Systems - Proceedings of the 7th International Workshop on Structural Health Monitoring, IWSHM 2009 (Vol. 1, pp. 569-576). DEStech Publications.