Vibration-based damage detection in flexible risers usingtime series analysis

Carlos Riveros, Tomoaki Utsunomiya, Katsuya Maeda, Kazuaki Itoh

Research output: Contribution to journalArticle

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. Amodal-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
JournalStructural Engineering/Earthquake Engineering
Volume24
Issue number2
DOIs
Publication statusPublished - Dec 14 2007

Fingerprint

Damage detection
riser
Time series analysis
vibration
time series analysis
damage
Fatigue damage
Pattern recognition
Dynamic response
Deterioration
pattern recognition
detection method
prediction
dynamic response
fatigue
detection
analysis
Damage
Time Series Analysis
Prediction

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Arts and Humanities (miscellaneous)
  • Geotechnical Engineering and Engineering Geology

Cite this

Vibration-based damage detection in flexible risers usingtime series analysis. / Riveros, Carlos; Utsunomiya, Tomoaki; Maeda, Katsuya; Itoh, Kazuaki.

In: Structural Engineering/Earthquake Engineering, Vol. 24, No. 2, 14.12.2007.

Research output: Contribution to journalArticle

@article{a31cec9264db462693dbe7cfcda2c17c,
title = "Vibration-based damage detection in flexible risers usingtime series analysis",
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. Amodal-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.",
author = "Carlos Riveros and Tomoaki Utsunomiya and Katsuya Maeda and Kazuaki Itoh",
year = "2007",
month = "12",
day = "14",
doi = "10.2208/jsceseee.24.62s",
language = "English",
volume = "24",
journal = "Structural Engineering/Earthquake Engineering",
issn = "0289-8063",
publisher = "Japan Society of Civil Engineers",
number = "2",

}

TY - JOUR

T1 - Vibration-based damage detection in flexible risers usingtime series analysis

AU - Riveros, Carlos

AU - Utsunomiya, Tomoaki

AU - Maeda, Katsuya

AU - Itoh, Kazuaki

PY - 2007/12/14

Y1 - 2007/12/14

N2 - 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. Amodal-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.

AB - 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. Amodal-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.

UR - http://www.scopus.com/inward/record.url?scp=36849051807&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=36849051807&partnerID=8YFLogxK

U2 - 10.2208/jsceseee.24.62s

DO - 10.2208/jsceseee.24.62s

M3 - Article

AN - SCOPUS:36849051807

VL - 24

JO - Structural Engineering/Earthquake Engineering

JF - Structural Engineering/Earthquake Engineering

SN - 0289-8063

IS - 2

ER -