Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)

In this study, a framework using Long Short Term Memory (LSTM) for prediction of reliability of Dynamic Positioning (DP) sub-systems for computation of Dynamic Positioning Reliability Index (DP-RI) has been proposed. The DP System is complex with significant levels of integration between many sub-sy...

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Published in:Volume 1: Offshore Technology
Main Authors: Fernandez, Charles, Bhushan Kumar, Shashi, Woo, Wai Lok, Norman, Rosemary, Kr. Dev, Arun
Format: Book Part
Language:English
Published: American Society of Mechanical Engineers (ASME) 2020
Subjects:
Online Access:https://nrl.northumbria.ac.uk/id/eprint/45287/
https://doi.org/10.1115/omae2020-18844
https://nrl.northumbria.ac.uk/id/eprint/45287/1/Real-time%20prediction%20of%20DP-RI%20using%20LSTM%202020.pdf
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spelling ftunivnorthumb:oai:nrl.northumbria.ac.uk:45287 2023-05-15T14:27:40+02:00 Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM) Fernandez, Charles Bhushan Kumar, Shashi Woo, Wai Lok Norman, Rosemary Kr. Dev, Arun 2020-08-03 text https://nrl.northumbria.ac.uk/id/eprint/45287/ https://doi.org/10.1115/omae2020-18844 https://nrl.northumbria.ac.uk/id/eprint/45287/1/Real-time%20prediction%20of%20DP-RI%20using%20LSTM%202020.pdf en eng American Society of Mechanical Engineers (ASME) https://nrl.northumbria.ac.uk/id/eprint/45287/1/Real-time%20prediction%20of%20DP-RI%20using%20LSTM%202020.pdf Fernandez, Charles, Bhushan Kumar, Shashi, Woo, Wai Lok, Norman, Rosemary and Kr. Dev, Arun (2020) Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM). In: International Conference on Offshore Mechanics and Arctic Engineering: Offshore Technology: Artificial Intelligence and Neural Networks in Offshore Technology. American Society of Mechanical Engineers (ASME), New York, V001T01A059. ISBN 9780791884317 G400 Computer Science Book Section PeerReviewed 2020 ftunivnorthumb https://doi.org/10.1115/omae2020-18844 2022-12-22T23:31:00Z In this study, a framework using Long Short Term Memory (LSTM) for prediction of reliability of Dynamic Positioning (DP) sub-systems for computation of Dynamic Positioning Reliability Index (DP-RI) has been proposed. The DP System is complex with significant levels of integration between many sub-systems such as the Reference System, DP Control System, Thruster / Propulsion System, Power System, Electrical System and the Environment System to perform diverse control functions. The proposed framework includes a mathematical computation approach to compute reliability of DP sub-systems and a data driven approach to predict the reliability at a sub-system level for evaluation of model performance and accuracy. The framework results demonstrate excellent performance under a wide range of data availability and guaranteed lower computational burden for real-time non-linear optimization. There are three main components of the proposed architecture for the mathematical formulation of the DP sub-systems based on individual sensor arrangements within the sub-system, computation of reliability of sub-systems and optimized LSTM deep learning algorithm for prediction of its reliability. Firstly, the mathematical formulation for the reliability of sub-systems is determined based on the series/parallel arrangement of the sensors of each individual equipment item within the sub-systems. Secondly, the computation of the reliability of sub-systems is achieved through an integrated approach during complex operation of the vessel. Thirdly, the novel optimized LSTM network is constructed to predict the reliability of the subsystems while minimizing integral errors in the algorithm. In this paper, numerical simulations are set-up using a state-of-the-art advisory decision-making tool with mock-up and real-world data to give insights into the model performance and validate it against the existing risk assessment methodologies. Furthermore, we have analyzed the efficiency and stability of the proposed model against various levels of ... Book Part Arctic Northumbria University, Newcastle: Northumbria Research Link (NRL) Volume 1: Offshore Technology
institution Open Polar
collection Northumbria University, Newcastle: Northumbria Research Link (NRL)
op_collection_id ftunivnorthumb
language English
topic G400 Computer Science
spellingShingle G400 Computer Science
Fernandez, Charles
Bhushan Kumar, Shashi
Woo, Wai Lok
Norman, Rosemary
Kr. Dev, Arun
Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)
topic_facet G400 Computer Science
description In this study, a framework using Long Short Term Memory (LSTM) for prediction of reliability of Dynamic Positioning (DP) sub-systems for computation of Dynamic Positioning Reliability Index (DP-RI) has been proposed. The DP System is complex with significant levels of integration between many sub-systems such as the Reference System, DP Control System, Thruster / Propulsion System, Power System, Electrical System and the Environment System to perform diverse control functions. The proposed framework includes a mathematical computation approach to compute reliability of DP sub-systems and a data driven approach to predict the reliability at a sub-system level for evaluation of model performance and accuracy. The framework results demonstrate excellent performance under a wide range of data availability and guaranteed lower computational burden for real-time non-linear optimization. There are three main components of the proposed architecture for the mathematical formulation of the DP sub-systems based on individual sensor arrangements within the sub-system, computation of reliability of sub-systems and optimized LSTM deep learning algorithm for prediction of its reliability. Firstly, the mathematical formulation for the reliability of sub-systems is determined based on the series/parallel arrangement of the sensors of each individual equipment item within the sub-systems. Secondly, the computation of the reliability of sub-systems is achieved through an integrated approach during complex operation of the vessel. Thirdly, the novel optimized LSTM network is constructed to predict the reliability of the subsystems while minimizing integral errors in the algorithm. In this paper, numerical simulations are set-up using a state-of-the-art advisory decision-making tool with mock-up and real-world data to give insights into the model performance and validate it against the existing risk assessment methodologies. Furthermore, we have analyzed the efficiency and stability of the proposed model against various levels of ...
format Book Part
author Fernandez, Charles
Bhushan Kumar, Shashi
Woo, Wai Lok
Norman, Rosemary
Kr. Dev, Arun
author_facet Fernandez, Charles
Bhushan Kumar, Shashi
Woo, Wai Lok
Norman, Rosemary
Kr. Dev, Arun
author_sort Fernandez, Charles
title Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)
title_short Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)
title_full Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)
title_fullStr Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)
title_full_unstemmed Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)
title_sort real-time prediction of reliability of dynamic positioning sub-systems for computation of dynamic positioning reliability index (dp-ri) using long short term memory (lstm)
publisher American Society of Mechanical Engineers (ASME)
publishDate 2020
url https://nrl.northumbria.ac.uk/id/eprint/45287/
https://doi.org/10.1115/omae2020-18844
https://nrl.northumbria.ac.uk/id/eprint/45287/1/Real-time%20prediction%20of%20DP-RI%20using%20LSTM%202020.pdf
genre Arctic
genre_facet Arctic
op_relation https://nrl.northumbria.ac.uk/id/eprint/45287/1/Real-time%20prediction%20of%20DP-RI%20using%20LSTM%202020.pdf
Fernandez, Charles, Bhushan Kumar, Shashi, Woo, Wai Lok, Norman, Rosemary and Kr. Dev, Arun (2020) Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM). In: International Conference on Offshore Mechanics and Arctic Engineering: Offshore Technology: Artificial Intelligence and Neural Networks in Offshore Technology. American Society of Mechanical Engineers (ASME), New York, V001T01A059. ISBN 9780791884317
op_doi https://doi.org/10.1115/omae2020-18844
container_title Volume 1: Offshore Technology
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