Mooring Integrity and Machine Learning

Prislin, Igor; Maroju, Soma

doi:10.4043/27866-ms

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…The result showed both methods performed well in predicting when changes in the mooring state occur. Also, in [15] a novel concept with regards to the integrity of the mooring system was proposed. They implemented a system named Position Response Learning System (PRLS) that, at its core, uses MLP for predicting the integrity of a mooring system.…”

Section: Related Workmentioning

confidence: 99%

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Using Neural Network Approaches to Detect Mooring Line Failure

et al. 2021

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The mooring systems give stability to the floating platforms against environmental conditions, stabilizing the platform with mooring lines attached to the seabed. The mooring systems are among the main components that guarantee the safety of the staff and the various operations carried out on the platforms. The current approaches used to monitor mooring lines are inefficient as line tension sensors are expensive to install, maintain, and have durability problems. This article presents the development of two neural networkbased machine learning systems: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM). They are able to detect mooring line failure in near real-time based on the comparison between measured and predicted motion. The implemented systems were trained and evaluated with simulated motion data generated using real environmental conditions measured in the Campos Basin, in Rio de Janeiro, Brazil. The results showed the MLP and LSTM models were able to detect a failure in the mooring lines, with increasing difference between the predicted and the measured motions when there is a line breakage. A comparison between the two machine learning models revealed the LSTM model performed better at predicting the motions of the platform. INDEX TERMSMooring line failure, failure detection, machine learning, neural networks, floating production storage and offloading.

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Section: Related Workmentioning

confidence: 99%

“…FIGURE15. Illustration of LSTM prediction on a single environmental condition with all mooring lines intact.…”

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confidence: 99%

Using Neural Network Approaches to Detect Mooring Line Failure

et al. 2021

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“…In the literature, it is possible to find a large body of work addressing MoL failures [16], [17], [8], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30]. The primary mechanical failure mechanisms in mooring systems are extreme load and fatigue, both of which are functions of the axial tension [17].…”

Section: Introductionmentioning

confidence: 99%

“…DDMs require a single intensive learning phase (i.e., model construction) and benefit from a computationally inexpensive forward phase (i.e., model used as a predictor) [33], making them well suited to develop DTs. [26], [27], [28], [29] have all proposed using DDMs to monitor other marine energy systems (Floating platforms, FPSO vessels, etc. ), and noted the potential for predictive models to reduce the cost of operational monitoring.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin of the Mooring Line Tension for Floating Offshore Wind Turbines

Walker

Coraddu

Oneto

et al. 2021

OCEANS 2021: San Diego – Porto

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The number of installed Floating Offshore Wind Turbines (FOWTs) has doubled since 2017, quadrupling the total installed capacity, and is expected to increase significantly over the next decade. Consequently, there is a growing consideration towards the main challenges for FOWT projects: monitoring the system's integrity, extending the lifespan of the components, and maintaining FOWTs safely at scale. Effectively and efficiently addressing these challenges would unlock the wide-scale deployment of FOWTs. In this work, we focus on one of the most critical components of the FOWTs, the Mooring Lines (MoLs), which are responsible for fixing the structure to the seabed. The primary mechanical failure mechanisms in MoLs are extreme load and fatigue, both of which are functions of the axial tension. An effective solution to detect long term drifts in the mechanical response of the MoLs is to develop a Digital Twin (DT) able to accurately predict the behaviour of the healthy system to compare with the actual one. Authors will leverage operational data collected from the world's first commercial floating wind farm (Hywind Pilot Park 1 ) in 2018, to investigate the effectiveness of the DT for the prediction of the MoL axial tension. The DT will be developed using state-of-the-art data-driven methods, and results based on real operational data will support our proposal.

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“…The FPSO of interest is currently mooring in the Australian North-West Shelf, one of Australia's most economically significant maritime regions. This problem is selected due to both the recent industry interest in mooring integrity monitoring (Prislin et al, 2017), and the computational cost of the simulator. The emulation methodology presented can theoretically be used to predict any continuous univariate output from the simulator, of which we analyse the mean offset amplitude from the simulator's stochastic time-domain output.…”

Section: Introductionmentioning

confidence: 99%

Emulation of vessel motion simulators for computationally efficient uncertainty quantification

et al. 2019

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The development and use of numerical simulators to predict vessel motions is essential to design and operational decision making in offshore engineering. Increasingly, probabilistic analyses of these simulators are being used to quantify prediction uncertainty. In practice, obtaining the required number of model evaluations may be prohibited by time and computational constraints. Emulation reduces the computational burden by forming a statistical surrogate of the model. The method is Bayesian and treats the numerical simulator as an unknown function modelled by a Gaussian process prior, with covariances of the model outputs constructed as a function of the covariances of the inputs. In offshore engineering, simulator inputs include directional quantities and we describe a way to build this information into the covariance structure. The methodology is discussed with reference to a numerical simulator which computes the mean turret offset amplitude of a FPSO in response to environmental forcing. It is demonstrated through statistical diagnostics that the emulator is well designed, with evaluations executed around 60,000 times faster than the numeric simulator. The method is generalisable to many offshore engineering numerical simulators that require directional inputs and is widely applicable to industry.

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Mooring Integrity and Machine Learning

Cited by 16 publications

References 8 publications

Using Neural Network Approaches to Detect Mooring Line Failure

Using Neural Network Approaches to Detect Mooring Line Failure

Digital Twin of the Mooring Line Tension for Floating Offshore Wind Turbines

Emulation of vessel motion simulators for computationally efficient uncertainty quantification

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