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.

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.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

Application of artificial neural network to loan recovery prediction

2016 ◽  
Vol 9 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Amos Olaolu Adewusi ◽  
Tunbosun Biodun Oyedokun ◽  
Mustapha Oyewole Bello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Type I ◽  
Ann Model ◽  
Content Type ◽  
Credit Evaluation ◽  
Artificial Neural

Purpose This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models. Design/methodology/approach Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples. Findings The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation. Originality/value The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.

scholarly journals Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

2014 ◽  
Vol 7 (4) ◽  
pp. 132-143
Author(s):  
ABBAS M. ABD ◽  
SAAD SH. SAMMEN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Water Reservoir ◽  
Prediction Method ◽  
Training Data ◽  
Ann Model ◽  
Data Set ◽  
Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

Artificial Neural Network for Predicting the Flow Behaviors of Hot Compressed 2124-T851 Aluminum Alloy

2010 ◽  
Vol 146-147 ◽  
pp. 720-723
Author(s):  
Yong Cheng Lin ◽  
Xiao Min Chen ◽  
Yu Chi Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Flow Stress ◽  
Strain Rate ◽  
Learning Algorithm ◽  
Ann Model ◽  
Wide Range ◽  
Feed Forward Network ◽  
Artificial Neural

The compressive deformation experiments of 2124-T851 aluminum alloy were carried out over a wide range of temperature and strain rate. An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the flow behaviors of hot compressed 2124-T851 aluminum alloy and working conditions. The input parameters of the model consist of strain rate, forming temperature and deformation degree whereas flow stress is the output. A three layer feed-forward network with 15 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. Good performance of the ANN model is achieved. The predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.

Failure pressure prediction of pipeline with single corrosion defect using artificial neural network

2021 ◽  
pp. 166-171
Author(s):  
К. Т. Чин ◽  
Т. Арумугам ◽  
С. Каруппанан ◽  
М. Овинис
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Failure Pressure ◽  
Corrosion Defect ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Описываются разработка и применение искусственной нейронной сети (ИНС) для прогнозирования предельного давления трубопровода с точечным коррозионным дефектом, подверженного воздействию только внутреннего давления. Модель ИНС разработана на основе данных, полученных по результатам множественных полномасштабных испытаний на разрыв труб API 5L (класс от X42 до X100). Качество работы модели ИНС проверено в сравнении с данными для обучения, получен коэффициент детерминации R = 0,99. Модель дополнительно протестирована с учетом данных о предельном давлении корродированных труб API 5L X52 и X80. Установлено, что разработанная модель ИНС позволяет прогнозировать предельное давление с приемлемой погрешностью. С использованием данной модели проведена оценка влияния длины и глубины коррозионных дефектов на предельное давление. Выявлено, что глубина коррозии является более значимым фактором разрушения корродированного трубопровода. This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination ( R ) of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error. Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

Data-Based Modeling for Reactive Ion Etching: Effectiveness of an Artificial Neural Network Model for Estimating Tungsten Silicon Nitride Etch Rate

2020 ◽  
Author(s):  
Madeline A. Walters ◽  
Zhaoyan Fan ◽  
Burak Sencer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Etch Rate ◽  
Training Data ◽  
Base Layer ◽  
Chamber Pressure ◽  
Ann Model ◽  
Lower Electrode ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract This paper presents a data-based approach for modeling a plasma etch process by estimating etch rate based on controlled input parameters. This work seeks to use an Artificial Neural Network (ANN) model to correlate controlled tool parameters with etch rate and uniformity for a blanket 1100 Å WSiN thin film using Cl2 and BCl3 chemistry. Experimental data was collected using a Lam 9600 PTX plasma metal etch chamber in an industrial cleanroom. The WSiN film was deposited over 3000 Å TEOS to ensure adhesion, with an 8-inch bare silicon wafer as the base layer. Controlled tool parameters were radio frequency (RF) upper electrode power, RF lower electrode power, Cl2 gas flowrate, BCl3 gas flowrate, and chamber pressure. The full factorial design of experiment method was used to select the combinations of experimental configurations. The ANN model was validated using a subset of the training data.

Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment

2013 ◽  
Vol 465-466 ◽  
pp. 1149-1154
Author(s):  
Ibrahim Masood ◽  
Nadia Zulikha Zainal Abidin ◽  
Nur Rashida Roshidi ◽  
Noor Azlina Rejab ◽  
Mohd Faizal Johari
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Recognition Performance ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Multivariate Quality Control ◽  
Artificial Neural ◽  
Full Factorial

Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control. Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically. In this research, full factorial design of experiment was utilized for investigating the effect of four design parameters, i.e., recognition window size, training data amount, training data quality and hidden neuron amount. The pattern recognition study focuses on bivariate correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shifts, μ = ± 0.75 ~ 3.00 standard deviations. Raw data was used as input representation for a generalized model ANN classifier. The findings suggested that: (i) the best performance for each pattern could be achieved by setting different design parameters through specific classifiers, which (ii) gave superior result (average RP = 98.85%) compared to an empirical design (average RP = 96.5%). This research has provided a new perspective in designing ANN pattern recognition scheme in the field of statistical process control.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

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