Damage detection in flexible risers using statistical pattern recognition techniques

Carlos Alberto Riveros, Tomoaki Utsunomiya, Katsuya Maeda, Kazuaki Itoh

研究成果: ジャーナルへの寄稿学術誌査読

8 被引用数 (Scopus)


A statistical pattern recognition technique based on time series analysis of vibration data is presented in this paper. A 20-m riser model experimentally validated is used for the numerical implementation of this technique. The dynamic response of the riser model is assessed using a semi-empirical approach with an increased mean drag coefficient model during lock-in events. Because structural damage is associated with fatigue damage, hinge connections are used to represent several damage scenarios. Then, the statistical pattern recognition technique is used to identify and locate structural damage using vibration data collected from strategically located sensors. Sensor locations are obtained from an optimum sensor placement method. The numerical results show that structural degradation due to fatigue in oscillating flexible risers can be assessed using the presented statistical pattern recognition technique.

ジャーナルInternational Journal of Offshore and Polar Engineering
出版ステータス出版済み - 3月 2008

!!!All Science Journal Classification (ASJC) codes

  • 土木構造工学
  • 海洋工学
  • 機械工学


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