Multi-condition hawser tension prediction of offshore offloading system based on long and short-term memory network and transfer learning

Abstract

Real-time prediction of hawser tension of the side-by-side oil offloading system of Floating Production Storage and Offloading (FPSO) can provide early warnings for hawser breakages and ship collision risk, thus improving structural, property, and environmental security. Although offline numerical models and Long Short-Term Memory (LSTM) could offer substantial precision, they confront intensive time costs in recalibrating or retraining models. This paper proposes an integration method of LSTM networks and transfer learning for real-time tension prediction considering inputs of actual remote wave elevations. We use short-term environmental data and numerical simulation data of correlated hawser tensions during offloading operations to train a pre-trained benchmark model for transfer learning. Then, a highly generalized and efficient transferred model is constructed by using a small sample to realize short-term tension predictions in time-varying environments. The results show the occurrence time and value of extreme tensions predicted by transfer learning nearly match the reference data, and their maximum errors are 3 s and 0.11, respectively, superior to LSTM direct training. Therefore, it provides sufficient demand for real-time prediction and early collision risk prevention in dynamically changing ocean environment. The research results could provide an alternative framework for intelligent monitoring of large-scale marine structures.

Keywords

• Hawser tension
• LSTM
• transfer learning
• FPSO
• real-time condition monitoring
• short-term prediction