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)

Abstract

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 data availability. In conclusion the prediction accuracy of the proposed model is scalable and higher when compared with other model results.