Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data

TL;DR

This paper presents a neural network-based method for sea state estimation using vessel response spectral data, demonstrating effective predictions with small vessels and multiple degrees of freedom, advancing the SAWB approach.

Contribution

The study introduces a model-free neural network approach for sea state estimation using vessel spectral responses, improving accuracy with multiple vessel DOFs and demonstrating feasibility with small vessels.

Findings

Strong correlation between heave responses and wave height estimates

Improved accuracy with multiple vessel DOFs (heave, pitch, roll)

Reasonable SSE accuracy even in low response scenarios

Abstract

The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel response spectral data to statistical wave properties for a small uninhabited surface vessel. Results showed a strong correlation between heave responses and significant wave height estimates, whilst the accuracy of mean wave period and wave heading predictions were observed to improve considerably when data from multiple vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and roll) NNs for SSE were shown to perform well when compared to existing SSE approaches that use similar simulation setups. One advantage of using small vessels for SAWB was shown as SSE accuracy was reasonable even when motion responses were low (in high-frequency, low wave height sea states). Given the information-dense statistical representation of vessel motion responses in spectral form, as well as the ability of NNs to effectively model complex relationships between variables, the designed SSE method shows promise for future adaptation to mobile SSE systems using the SAWB approach.