Vibration-Based Damage Detection in Flexible Risers Using Time Series Analysis

Riveros, Carlos Alberto Díaz; Utsunomiya, Tomoaki; Maeda, Kazuyuki; Ito, Kazuaki

doi:10.2208/jsceja.63.423

Cited by 7 publications

(4 citation statements)

References 15 publications

(15 reference statements)

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“…Similarly, a modal distribution method was proposed by Sweetman et al [16], which uses the change between the power spectrums from measured structural responses to indicate damage in the primary system; the method is a frequency domain method based on output data, which is not suitable to deepwater risers with time varying natural frequencies. Riveros et al [17] concluded that a statistical pattern Pipeline failure statistics recognition method with a combined model with auto-regressive (AR) and auto-regressive with exogenous inputs (ARX) works better than a modal distribution method. However, in deepwater, the inputs are unknown, which means the method is not suitable.…”

Section: Methods For Riser Damage Detectionmentioning

confidence: 99%

Robotic MFL Sensor and Vibration based Deepwater Riser Monitoring System

Huang

Nagarajaiah

2012

All Days

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The safety of deepwater risers is essential for successful operation of offshore platforms. In this paper, new structural health monitoring (SHM) techniques for deepwater risers is developed to monitor damage due to vibration and fatigue. The global monitoring is achieved using the proposed blind identification technique based on vibrations. First, the location of the fatigue damage is determined by the proposed blind identification technique. The global monitoring is used to identify the dominant modes of vibration, from which, the likely location of fatigue damage is estimated. The proposed method is validated using Gulfstream test data from MIT. Second, extent of damage is determined by local monitoring using robotic crawler with magnetic flux leakage (MFL) sensor. The effectiveness of the local monitoring with MFL sensor is verified by experiments.Combining the proposed global and local monitoring techniques can enhance fatigue damage estimation. Local inspection using MFL sensor is power efficient and yields high resolution results that can estimate the extent of damage. Proposed fatigue damage estimation technique can satisfactorily predict location of global damage.The proposed SHM techniques are for well suited for deepwater risers. Robotic MFL sensor can be used for in-line inspection for pipelines. Proposed fatigue damage estimation method can be adapted to other offshore floating systems.

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Section: Methods For Riser Damage Detectionmentioning

confidence: 99%

Robotic MFL Sensor and Vibration based Deepwater Riser Monitoring System

Huang

Nagarajaiah

2012

All Days

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“…19 However, the vast majority of these studies have primarily treated the data from each sensor as an independent measurement to construct scalar autoregressive (AR) models. 7,9,16,[19][20][21][22][23][24] Mattson and Pandit 25 proposed a method based on ARV models and using the statistical moments of the residuals of these models as damage-sensitive features. Vector models allow a particular series to be described not only in terms of its own past values, but also in terms of the past values in the other sensors, and provides a thorough description of the interaction between response sensors.…”

Section: Statistical Damage Detection Motivationmentioning

confidence: 99%

Reliability analysis and damage detection in high-speed naval craft based on structural health monitoring data

Okasha

Frangopol

Saydam

et al. 2010

Structural Health Monitoring

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Current and future trends in naval craft design are leaning toward the development of high-speed and high-performance vessels. Lack of information on wave-induced loads for these vessels presents a challenge in ensuring their safety that is best tackled with monitoring operational loads and detecting damage via structural health monitoring (SHM) systems. These monitoring systems, however, require efficient statistical and probabilistic procedures that are able to effectively treat the uncertainties inherent in the massive volumes of collected data and provide interpretable information regarding the reliability and condition of the craft structure. In this article, an approach for using SHM data in the reliability analysis and damage detection in high-speed naval craft (HSNC) under uncertainty is presented. This statistical damage detection technique makes use of vector autoregressive modeling for detection and localization of damage in the ship structure. The methodology is illustrated on an HSNC, HSV-2. Data obtained from seakeeping trials of HSV-2 were treated as the SHM data mentioned above.

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“…Bao et al 17 used the autoregressive moving average (ARMA) model for the response analysis of submarine pipelines. Similar to the work of Riveros et al, 16 the Mahalanobis distance between the AR model coefficient vector obtained from the healthy and damaged structure is used as the damage indicator. Besides, a realistic experimental simulation of ambient excitations was attempted by placing a 10-m long pipeline in a water tank so that random wave loading following a JONSWAP spectrum was generated.…”

Section: Introductionmentioning

confidence: 99%

“…The time series analysis methods have also been used for the damage identification of deepsea risers. Riveros et al 16 combined the AR and ARX models to fit the responses of an experimental riser. A statistical pattern recognition method with the estimated AR coefficients was used to detect and locate the structural deterioration of the riser due to fatigue damage.…”

Section: Introductionmentioning

confidence: 99%

Output-only structural damage localization for deepsea mining risers using cointegration analysis

Wang

et al. 2022

Structural Health Monitoring

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Deepsea mining risers, which transport mineral resources from the seafloor to surface facilities, are critical components of deepsea mining systems. This article presents a novel cointegration-based method for localizing structural damage in risers subjected to random excitations from waves, currents, and moving floating facilities. Although the dynamic responses of a riser (usually consisting of multiple segments) are often nonstationary, those within an individual pipe segment often have synchronous fluctuations with a common trend, allowing the application of a multivariate time series analysis. Cointegration can thus be performed for a pipe segment by treating the internal responses as the endogenous variables and those of the other pipe segments as the exogenous variables. Consequently, a stationary cointegrating residual for the dynamic responses purged of the influence of nonstationary excitations can be obtained. The cointegrating residual in conjunction with a statistical hypothesis test scheme is used to create an output-only damage indicator. The effectiveness of the proposed damage localization method is demonstrated with a numerical and experimental multi-segment riser. The results indicate that the strain responses of both risers exhibit nonstationary fluctuations due to the changeable environmental excitations. As the proposed method is robust to the external loading, it can accurately identify the damage location.

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Vibration-Based Damage Detection in Flexible Risers Using Time Series Analysis

Cited by 7 publications

References 15 publications

Robotic MFL Sensor and Vibration based Deepwater Riser Monitoring System

Robotic MFL Sensor and Vibration based Deepwater Riser Monitoring System

Reliability analysis and damage detection in high-speed naval craft based on structural health monitoring data

Output-only structural damage localization for deepsea mining risers using cointegration analysis

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