Vibration-based damage detection in flexible risers using time series analysis

Carlos Riveros, Tomoaki Utsunomiya, Katsuya Maeda, Kazuaki Itoh

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In this paper, a statistical pattern recognition method based on time series analysis is implemented in flexible risers. This method uses a combination of Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) prediction models. The flexible riser model used in this paper is experimentally validated employing a proposed numerical scheme for dynamic response of flexible risers. A modal-based damage detection approach is also implemented in the flexible riser model and its results are compared with the ones obtained from time series analysis. The numerical results show that the time series analysis presented in this paper is able to detect and locate structural deterioration related to fatigue damage in flexible risers. Finally, considering the case study results presented in this paper, the presented AR-ARX prediction model works better than the modal-based damage detection method.

Original languageEnglish
Pages (from-to)423-433
Number of pages11
JournalDoboku Gakkai Ronbunshuu A
Volume63
Issue number3
DOIs
Publication statusPublished - Dec 1 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanics of Materials
  • Mechanical Engineering

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