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

Carlos Riveros, Tomoaki Utsunomiya, Katsuya Maeda, Kazuaki Itoh

研究成果: Contribution to journalArticle査読

9 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)423-433
ページ数11
ジャーナルDoboku Gakkai Ronbunshuu A
63
3
DOI
出版ステータス出版済み - 12 1 2007
外部発表はい

All Science Journal Classification (ASJC) codes

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

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