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

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 esti...

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Bibliographic Details
Published inarXiv.org
Main Authors Long, Nathan K, Sgarioto, Daniel, Garratt, Matthew, Sammut, Karl
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.08.2022
Subjects
Artificial neural networks
Complex variables
Degrees of freedom
Heaving
Machine learning
Neural networks
Pitch (inclination)
Rolling motion
Sea states
State estimation
Vessels
Wave height
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Summary: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.
ISSN:2331-8422