Probabilistic fatigue analysis of marine structures using the univariate dimension-reduction method

Monsalve-Giraldo, J.S.; Dantas, Claudio Marcio Silva; Sagrilo, Luís Volnei Sudati

doi:10.1016/j.marstruc.2016.07.008

Cited by 17 publications

(1 citation statement)

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“…One of the major problems in the design of offshore equipment is fatigue damage accumulation. Although this topic has been extensively studied in the literature, theoretically, numerically, and experimentally [3][4][5][6][7][8][9], the available inspection and monitoring technologies developed to date have not been able to fully overcome the severe environmental challenges associated with offshore service activities. Remotely operated vehicles (ROVs) have been widely used since the 1970s but face serious difficulties, despite technological advances, due to the highly unpredictable operating environment characterised by poor visibility and unstable conditions [10,11].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Monitoring of Fatigue Crack Growth in Mooring Chains

et al. 2019

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Offshore installations are subject to perpetual fatigue loading and are usually very hard to inspect. Close visual inspection from the turret is usually too hazardous for divers and is not possible with remotely operated vehicles (ROVs) because of the limited access. Conventional nondestructive techniques (NDTs) have been used in the past to carry out inspections of mooring chains, floating production storage and offloading systems (FPSOs), and other platforms. Although these have been successful at detecting and assessing fatigue cracks, the hazardous nature of the operations calls for remote techniques that could be applied continuously to identify damage initiation and progress. The aim of the present work is to study the capabilities of acoustic emission (AE) as a monitoring tool to detect fatigue crack initiation and propagation in mooring chains. A 72-day large-scale experiment was designed for this purpose. A detailed analysis of the different AE signal time domain features was not conclusive, possibly due to the high level of noise. However, the frequency content of the AE signals offers a promising indication of fatigue crack growth.

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