Innovative load identification with Res-UNet: Integrating phase space reconstruction and physics-informed deep learning

Abstract

This study developed a specialized load identification model for a specific ocean platform that integrates advanced deep learning techniques with the unique physical characteristics of marine engineering. Key achievements include high-dimensional mapping of data features using phase space reconstruction (PSR) techniques to enhance the predictive capacity of the model through chaos system theory; optimization of ResNet and UNet deep learning networks with attention mechanisms to boost performance; calculation of stiffness, mass, and damping matrices using finite element software; establishment of corresponding state-space equations; and integration of structural dynamics equations with the deep learning network loss function to significantly improve load identification accuracy. In addition, proprietary structural equations for ocean platform structures were developed, underscoring the uniqueness and applicability of the algorithm. The proposed physics-informed Res-UNet model exhibited excellent performance in identifying both periodic and random loads. These improvements were validated through training with finite element simulated data and subsequent experimental applications, demonstrating the effectiveness of the proposed innovative methods.