Sensitivity Analysis of an ANN-Based System for Detection of Mooring Line Failure

2020 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Nicolas Tcherniguin ◽  
Philippe Bouchard ◽  
Ho-Joon Lim
Keyword(s):  
Numerical Simulations ◽  
Compressive Load ◽  
Low Frequency ◽  
Mooring Line ◽  
Offshore Platforms ◽  
Ann Model ◽  
Monitoring Equipment ◽  
Mooring Lines ◽  
Inclination Angles ◽  
Noise Tolerant

Abstract Monitoring the integrity of mooring lines on floating offshore platforms is one of the key factors in ensuring safe and productive offshore operations. Sensors, such as inclinometers, compressive load cells or strain sensors, can be used to monitor the inclination angles or tensions on mooring lines. An alternative method using only dry monitoring systems, such as DGPS (Differential Global Positioning System), Gyrocompass and/or IMU (Inertial Measurement / Motion Unit), can also be used to monitor the integrity of mooring lines. This method uses the measured motions and positions of a vessel without any information on the environmental conditions to detect mooring line failure. The detection of mooring line failure is based on detecting shifts in low-frequency periods and mean yaw angles as a function of vessel position, mass and added mass. The proposed method utilizes Artificial Neural Network (ANN) to recognize and classify patterns. The training of an ANN model requires examples/data associated with intact mooring lines and broken mooring line(s). Examples/data of broken mooring line(s) are practically available only from numerical simulations. Therefore, it is important to address these two key topics: (1) Is the real behavior of the floating offshore platform sufficiently aligned with numerical simulations? and (2) The effect of the accuracy of monitoring equipment on the performance of an ANN-based system. The first topic is reviewed briefly with its possible solution including some sensitivity tests, and this paper focuses on addressing the second topic. A system architecture is discussed in this paper along with the accuracy of the monitoring equipment. As an example, an ANN model has been trained to detect a broken mooring line of a spread-moored FPSO. This ANN model has been tested on its performance in dealing with a range of possible errors associated with the monitoring equipment. Furthermore, the tests have been carried out for a combination of variables that are not included in the ANN training, such as: vessel draft (mass), sea state conditions and directions. This paper presents the results of the tests for various variable sensitivities, which cover vessel positions, mean yaw angles and vessel drafts. These are essentially testing the tolerance of a trained ANN model against error or noise in the data. The results show that a trained ANN model can be error/noise tolerant.

Prediction of Offshore Platform Mooring Line Tensions Using Artificial Neural Network

2017 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Johyun Kyoung ◽  
Jim O’Sullivan ◽  
Kostas F. Lambrakos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Simulations ◽  
Offshore Platform ◽  
Mooring Line ◽  
Offshore Platforms ◽  
Ann Model ◽  
Station Keeping ◽  
Mooring Lines ◽  
Hidden Neurons

Station-keeping is one of the important factors in the design of offshore platforms. Some offshore platforms, such as Spar, Semi-submersible and FPSO, use mooring lines as a mean for station-keeping. Tensions in the mooring lines are one of the key factors in station-keeping. The design of an offshore platform and its mooring lines is based on computed motions of the platform and associated mooring line tensions from numerical simulations using a software code on the basis of metocean criteria. This paper presents an Artificial Neural Network (ANN) model for the prediction of mooring line tensions based on the motions of the platform. This ANN model is trained with time histories of vessel motions and corresponding mooring line tensions for a range of sea states from the results of numerical simulations. After the model is trained, it can reproduce with great fidelity and very fast the mooring line tensions. In addition, it can generate accurate mooring line tensions for sea states that were not included in the training, and this demonstrates that the model has captured the knowledge for the underlying physics between vessel motions and mooring line tensions. The paper presents an example of the training and the validation of the model for a semi-submersible offshore platform for a range of sea states. The training of the ANN model employed a back-propagation learning algorithm. In this algorithm the computed output error is back-propagated through the neural network to modify the connection weights between neurons. The training started with a small number of hidden neurons, and the model grew adaptively by adding hidden neurons until either the target output convergence is achieved or a maximum number of additional hidden neurons is reached. The ANN model discovers nonlinear relationships between the input and output variables during training. The paper presents comparison of time series of mooring line tensions for sea states that were and were not included in the training between those from the numerical simulations and those computed by the trained ANN model. Fatigue assessment is also used to quantitatively measure the accuracy of the ANN model prediction of the time series of mooring line tensions. The paper presents the results of fatigue assessment using various stages of the ANN models with different number of hidden neurons. This shows that the additional hidden neurons improve the prediction of the ANN model of the mooring line tensions for sea states that were and were not included in the training. This approach of prediction of mooring line tensions based on vessel motions using ANN model paves the way to the development of an ANN-based monitoring system. Also, this ANN study demonstrates a great potential for the use of a more general and comprehensive ANN model to help monitor the dynamic behavior of floating systems and forecast problems before they occur by detecting deviations in historic patterns.

Detection of Mooring Line Failure of a Spread-Moored FPSO: Part 1 — Development of an Artificial Neural Network Based Model

2019 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Ho-Joon Lim ◽  
Johyun Kyoung ◽  
Nicolas Tcherniguin ◽  
Timothee Lefebvre ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Simulations ◽  
Learning Algorithm ◽  
Offshore Platform ◽  
Training Data ◽  
Mooring Line ◽  
Ann Model ◽  
Mooring Lines ◽  
Artificial Neural

Abstract Artificial Intelligence (AI) has gained popularity in recent years for offshore engineering applications, and one such challenging application is detection of mooring line failure of a floating offshore platform. For most types of floating offshore platforms, accurately detecting any mooring line damage and/or failures is of great interest to their operators. This paper demonstrates the use of an Artificial Neural Network (ANN) model for detecting mooring line failure for a spread-moored FPSO. The ANN model representation, in terms of its input variables, is based on assessing when changes in a platform’s motion characteristics are in-fact indicators of a mooring line failure. The output of the ANN model indicates the status condition for the mooring lines (intact or failed). This ANN model only requires GPS / DGPS monitoring data and does not require data on the environmental conditions at the platform. Since the mass of an FPSO changes with the stored volume of oil, the vessel’s mass is also an input variable. The ANN training uses the results from numerical simulations of a spread-moored FPSO with fourteen mooring lines. The numerical simulations create the FPSO’s response to a range of metocean conditions for 360-degree directions, and they cover several levels of vessel draft (mass). Furthermore, the simulations cover both the intact mooring configuration and the full permutation where each of the fourteen mooring lines is modeled as broken at the top. The global performance analysis of the FPSO is presented in a different paper (Part 2 of these paper series). The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determining the size of ANN hidden layers. The trained ANN model can detect mooring line failure, even for vessel draft (mass), sea states and environmental directions that are not included in the training data. This demonstrates that the ANN model can recognize and classify patterns associated with mooring line failure and separate such patterns from those associated with intact mooring lines under conditions not included in the original training data. This study reveals a great potential for using an ANN model to monitor the station keeping integrity of a floating offshore platform with changing storage, or mass status, and to detect mooring line failure using only the vessel’s mass and deviations in the platform’s motions derived from GPS / DGPS data.

Damage Detection of Offshore Platform Mooring Line Using Artificial Neural Network

2018 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Jim O’Sullivan ◽  
Ho-Joon Lim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Total Mass ◽  
Offshore Platform ◽  
Mooring Line ◽  
Ann Model ◽  
Station Keeping ◽  
Mooring Lines ◽  
Artificial Neural

Station-keeping using mooring lines is an important part of the design of floating offshore platforms, and has been used on most types of floating platforms, such as Spar, Semi-submersible, and FPSO. It is of great interest to monitor the integrity of the mooring lines to detect any damaged and/or failures. This paper presents a method to train an Artificial Neural Network (ANN) model for damage detection of mooring lines based on a patented methodology that uses detection of subtle shifts in the long drift period of a moored floating vessel as an indicator of mooring line failure, using only GPS monitoring. In case of an FPSO, the total mass or weight of the vessel is also used as a variable. The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determination of ANN architecture. The input variables of the ANN model can be derived from the monitored motion of the platform by GPS (plus vessel’s total mass in case of an FPSO), and the output of the model is the identification of a specific damaged mooring line. The training and testing of the ANN model use the results of numerical analyses for a semi-submersible offshore platform with twenty mooring lines for a range of metocean conditions. The training data cover the cases of intact mooring lines and a damaged line for two selected adjacent lines. As an illustration, the evolution of the model at various training stages is presented in terms of its accuracy to detect and identify a damaged mooring line. After successful training, the trained model can detect with great fidelity and speed the damaged mooring line. In addition, it can detect accurately the damaged mooring line for sea states that are not included in the training. This demonstrates that the model can recognize and classify patterns associated with a damaged mooring line and separate them from patterns of intact mooring lines for sea states that are and are not included in the training. This study demonstrates a great potential for the use of a more general and comprehensive ANN model to help monitor the station keeping integrity of a floating offshore platform and the dynamic behavior of floating systems in order to forecast problems before they occur by detecting deviations in historical patterns.

Contribution of the Mooring System to the Low-Frequency Motions of a Semisubmersible in Combined Wave and Current

2009 ◽  
Author(s):  
Amany M. A. Hassan ◽  
Martin J. Downie ◽  
Atilla Incecik ◽  
R. Baarholm ◽  
P. A. Berthelsen ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Damping Ratio ◽  
Low Frequency ◽  
Scale Model ◽  
Mooring Line ◽  
Mooring System ◽  
Current Field ◽  
Mooring Lines ◽  
Mean Values ◽  
Low Frequency Motion

This paper presents the results of an experiment carried out on a semi-submersible model to measure the steady drift force and low frequency surge motions. In the experiments, the influence of mooring systems was also investigated in different combinations of current and sea state. The measurements were carried out with a 1/50 scale model which was moored using horizontal springs and catenary mooring lines. A comparative study of the mean values of steady drift motions and the standard deviation of the low frequency motion amplitudes is presented. In addition, the effect of current on the damping ratio is discussed. It is found that for both horizontal and catenary moorings, the presence of a current increases the damping ratio of the system. For the catenary mooring system, as expected, the presence of mooring lines and their interaction with waves and current increases the damping compared to the damping of the horizontal mooring system. The measured mean values of the surge motions in a wave–current field are compared to the superposed values of those obtained from waves and current separately. For the horizontal mooring, it is found that there is good agreement in moderate sea states, while in higher sea states the measured motion responses are larger. In the wave-current field, the standard deviation of the surge motion amplitudes is found to be less than that obtained in waves alone. This can be explained by the increased magnitude of the damping ratio. Only in the cases of high sea states with the horizontal mooring system, was it found that the standard deviation of the surge motions is slightly larger than those obtained for waves and current separately. This may be explained by the absence of catenary mooring line damping.

An AI-Based Detection System for Mooring Line Failure

2021 ◽  
Author(s):  
Djoni Eka Sidarta ◽  
Nicolas Tcherniguin ◽  
Philippe Bouchard ◽  
Ho-Joon Lim ◽  
Mengchen Kang ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Computational Time ◽  
Mooring Line ◽  
Ann Model ◽  
Mooring Lines ◽  
Hydrodynamic Simulations ◽  
Alternative Method ◽  
Detection Program ◽  
Artificial Neural ◽  
Fully Coupled

Abstract Safe and productive offshore operations are of utmost importance, with monitoring the integrity of mooring lines on floating offshore platforms being one of the key factors. The conventional method uses sensors installed on mooring components, which may fail over time and can be costly to replace. Alternative methods using dry and non-intrusive monitoring systems offer a lot of potentials to the industry. An alternative method that uses only Differential Global Positioning System (DGPS) data has been proposed by Sidarta et al. (2018, 2019), and it does not require any information on environmental conditions. This alternative method is based on monitoring shifts in the low-frequency periods and mean yaw angles as a function of vessel positions, mass and added mass. The method utilizes Artificial Intelligence, specifically Artificial Neural Network (ANN), for the detection of mooring line failure, which is a pattern recognition and classification problem. The ANN model learns to recognize and classify patterns of intact mooring lines and those of a broken line. One of the proposed models is a group identification model, in which the model identifies the mooring group that has a broken line. This paper shows that an ANN model can be quite robust and tolerant in dealing with conditions that are somewhat different from its training. As an example, an ANN model for detecting mooring line failure on a spread moored FPSO has been trained using MLTSIM hydrodynamic simulations with quasi static model of the mooring lines and risers to significantly reduce the computational time to generate the ANN training data. The trained ANN model can properly function when tested using fully coupled OrcaFlex hydrodynamic simulations with environmental conditions that are not included in the training. Moreover, although the ANN model has been trained using simulations with a completely removed line, the trained model can still function for a line broken at the bottom. This ANN model is an ANN-based status detection model, which is one of the key components in the ALANN (Anchor Lines monitoring using Artificial Neural Networks) System. The system also composes of an ANN-based system evaluation model, an algorithm-based status detection program and an event detection program. A series of fully coupled dynamic simulations have been used to test the ALANN System. Most of the simulations have a single mooring line failure that occurs randomly during simulation, and the failed line varies for different simulations. Each simulation lasts for six hours. The ALANN System uses a two-hour time window at a time and moves every 20 minutes. The tests demonstrate how each component of the ALANN System contributes to and improves the robustness of the overall solution.

Optimal Design Methodology for Multi-Component Mooring Systems in Deep Water

2010 ◽  
Author(s):  
Long Yu ◽  
Jiahua Tan
Keyword(s):  
Optimal Design ◽  
Deep Water ◽  
Design Methodology ◽  
Element Model ◽  
Gas Production ◽  
Mooring Line ◽  
Mooring System ◽  
Offshore Platforms ◽  
Mooring Lines ◽  
Restoring Force

Multi-component mooring systems, one of the crucial equipments of offshore platforms, play an important role in deep water oil&gas production because of relative low cost and light weight. A single mooring line can be constructed by combination of wire ropes, chains, fiber ropes, buoys and connectors etc. and provide adequate restoring force at fairlead point of platforms. Although the static and dynamic analyzing approaches for a determined multi-component system have been studied already, it is still hard to design and predetermine an appropriate mooring system that can satisfy the codes with multi-component lines. Referred to the conventional mooring system design method, this paper brings out an optimal design methodology for multi-component mooring systems. According to quasi-static method, at extreme offset position of the platform, an optimization model for designing the multi-component mooring line with biggest tension in deep water has been provided. Then, with the aid of design wave method and morison equation, a finite element model has been used to calculate mooring line dynamics at each fairlead point in time domain. The nonlinear interaction of mooring lines and seabed has also been investigated. Heave and surge of the platform have also been considered. Both 2D and 3D mooring system models have been built to search the interference of the lines and directional influence of environment loads like current and wave. The paper applied this set of analyzing methods and processes into a deep water semisubmersible serving at South China Sea. Compared with the results calculated by other software, the methodology mentioned in the paper got similar result with less weight and bigger restoring force.

scholarly journals Effect of Water Depths on the Hydrodynamic Responses of an FPSO Platform

2018 ◽  
Vol 203 ◽  
pp. 01022
Author(s):  
Matthew Guan ◽  
Montasir Osman Ahmed Ali ◽  
Cheng Yee Ng
Keyword(s):  
Oil And Gas ◽  
Response Spectrum ◽  
Low Frequency ◽  
Mooring Line ◽  
Random Wave ◽  
Statistical Parameters ◽  
Mooring Lines ◽  
Line System ◽  
Wave Condition ◽  
Water Depths

Ship-shaped Floating Production Storage Offloading platforms (FPSO) are commonly used in the production of oil and gas in offshore deepwater regions. The vessel is held in place by mooring lines anchored to the seabed during operation, either in spread or turret mooring arrangement. When designing such systems, water depth is a main factor that needs to be considered. At greater depths, the hydrodynamic properties of mooring lines become important and may not be accurately predicted through traditional experiments or numerical quasi-static models. Numerical simulation using coupled dynamic analysis is thus recommended, as the hull-mooring behaviour is analysed simultaneously, and the damping and added mass properties of the entire mooring line system is taken into account. This paper investigates the motions and mooring line tensions of a turret-moored FPSO at various water depths ranging from 1000 m to 2000 m. The analysis focuses on numerical simulations in the fully coupled dynamic time domain. The study utilizes the commercial software AQWA, with the FPSO model subjected to a unidirectional random wave condition. The hull hydrodynamics is first solved using the 3D radiation/diffraction panel method, and the hull response equation is then coupled with the mooring line equation. The dynamic motions and mooring line tensions results are presented in terms of statistical parameters as well as response spectrum. The results highlight the significance of greater water depths on low frequency responses in surge motions and mooring line tensions, and provides insight on the increasing and decreasing trend of these responses.

Experimental and Numerical Study on Large Truncation of Deepwater Mooring Line

2009 ◽  
Author(s):  
Yihua Su ◽  
Jianmin Yang ◽  
Longfei Xiao ◽  
Gang Chen
Keyword(s):  
Dynamic Response ◽  
Water Depth ◽  
Numerical Study ◽  
Unit Length ◽  
Low Frequency ◽  
Model Tests ◽  
Mooring Line ◽  
Mooring System ◽  
Mooring Lines ◽  
Wave Basin

Modeling the deepwater mooring system in present available basin using standard Froude scaling at an acceptable scale presents new challenges. A prospective method is to truncate the full-depth mooring lines and find an equivalent truncated mooring system that can reproduce both static and dynamic response of the full-depth mooring system, but large truncation arise if the water depth where the deepwater platform located is very deep or the available water depth of the basin is shallow. A Cell-Truss Spar operated in 1500m water depth is calibrated in a wave basin with 4m water depth. Large truncation arises even though a small model scale 1:100 is chosen. A series of truncated mooring lines are designed and investigated through numerical simulations, single line model tests and coupled wave basin model tests. It is found that dynamic response of the truncated mooring line can be enlarged by using larger diameter and mass per unit length in air. Although the truncated mooring line with clump presents a “taut” shape, its dynamic characteristics is dominated by the geometry stiffness and it underestimates dynamic response of the full-depth mooring line, even induces high-frequency dynamic response. There are still two obstacles in realizing dynamic similarity for the largely truncated mooring system: lower mean value of the top tension of upstream mooring lines, and smaller low-frequency mooring-induced damping.

Development of Data-Driven Models for Prediction of Mooring Line Tensions

2020 ◽  
Author(s):  
Nitesh Kumar ◽  
Ching Theng Liong ◽  
Wei Kean Chen ◽  
Allan Ross Magee ◽  
Kie Hian Chua ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Low Frequency ◽  
Data Driven ◽  
Training Dataset ◽  
Mooring Line ◽  
Test Cases ◽  
Floating Structure ◽  
Feed Forward Neural Network ◽  
Frequency Components ◽  
Ann Models

Abstract This paper describes the development of data-driven models for the prediction of mooring line tensions by separating the low- and wave-frequency components of the tensions, such that the former is approximated as quasi-static tensions while the latter is predicted using ANN models. A bilinear model is used to interpolate the low-frequency quasi-static tensions between known values in a look-up table while a feed forward neural network model that utilizes the fairlead motions as input is used to predict the tension dynamics at the fairlead. The ANN models are trained using the results from numerical simulations, such as those generated during the engineering design and construction stages of the floating structure. The predicted line tensions are compared with those obtained using coupled numerical simulations, including test cases for different wave realizations that are not included in the training dataset. Models trained for single and multiple directions of the environment are also assessed for prediction accuracy. Initial comparisons show that good predictions of line tensions can be obtained for certain environments using the proposed approach, thus demonstrating the potential for use in applications where real-time predictions are required to enhance the safety and / or reliability of mooring systems.

Hydrodynamic Study and Performance Analysis of the OC4-DeepCWind Platform by CFD Method

2020 ◽  
pp. 2050020
Author(s):  
Yang Huang ◽  
Yuan Zhuang ◽  
Decheng Wan
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Center Of Gravity ◽  
Mooring Line ◽  
Time Step ◽  
Mooring Lines ◽  
Frequency Wave ◽  
Nonlinear Motion ◽  
Motion Responses ◽  
Motion Performance

(1) The RAOs of OC4-DeepCWind platform motions are more sensitive to the low-frequency wave than the high-frequency wave. The nonlinear motion responses for platform heave and pitch motions are comparatively remarkable. (2) The pitch motion of OC4-DeepCWind platform is much more apparently influenced by the height of center of gravity (COG) than surge and heave motions. The lower COG height within a suitable range leads to a smaller fluctuation amplitude of platform pitch motion in waves. (3) A large horizontal displacement abruptly occurs to the OC4-DeepCWind platform when one mooring line is failure. The risk of failure for the other mooring lines significantly increases. To better understand the hydrodynamic performance of a floating support platform in various wave environments, a two-phase CFD solver naoe-FOAM-SJTU based on the open source CFD toolbox OpenFOAM is applied to investigate the hydrodynamic characteristics and motion performance of the OC4-DeepCWind platform. Moreover, the restoring force and moment of mooring lines are simulated using the solver in time domain. The studies of grid sensitivity and time step refinement are first conducted to determine an appropriate time step and mesh size. Then hydrodynamic responses of the floater in free-decay tests are analyzed and compared with experimental data, and the motion performance of the platform in regular waves with different parameters is also investigated. In addition, the platform motion responses with one mooring line broken and different heights of center of gravity are explored. It is shown that simulation results have good agreement with published data, and several conclusions can be drawn through the study. The RAOs of platform motions are found to be more sensitive to the low-frequency wave than the high-frequency wave. Nonlinear motion responses are comparatively remarkable in platform heave and pitch motions. Besides, the lower height of center of gravity within a suitable range is benefit to the stability of floating platform. Survival condition with broken mooring line should be paid enough attention to avoid the failure of other mooring lines.

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