Sea Spectral Estimation Using ARMA Models

Berardengo, Marta; Rossi, Giovanni Battista; Crenna, Francesco

doi:10.3390/s21134280

Cited by 15 publications

(9 citation statements)

References 36 publications

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“…The required spectra can be estimated in different ways [29][30][31]. In this study, Welch's overlapped segment averaging (WOSA) approach has been adopted [32].…”

Section: Spectrum Estimation Methodsmentioning

confidence: 99%

Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency

Rossi

Nardone

Settanta

et al. 2023

Sensors

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Technological development in terms of the power requirement for data acquisition and processing opens new perspectives in the field of environmental monitoring. Near real-time data flow about the sea condition and a possible direct interface with applications and services devoted to marine weather networks would have a significant impact on several aspects, such as, for example, safety and efficiency. In this scenario, the needs of buoy networks have been analyzed, and the estimation of directional wave spectra from buoys’ data has been deeply investigated. Two methods have been implemented, namely the truncated Fourier series and the weighted truncated Fourier series, and they have been tested by both simulated and real experimental data, representative of typical Mediterranean Sea conditions. From simulation, the second method proved to be more efficient. From the application to real case studies, it emerged that it works effectively in real conditions, as confirmed by parallel meteorological observations. The estimation of the main propagation direction was possible with a small uncertainty of a few degrees, yet the method exhibits a limited directional resolution, which suggests the need for undertaking further studies, briefly addressed in the conclusions.

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“…The required spectra can be estimated in different ways [29][30][31]. In this study, Welch's overlapped segment averaging (WOSA) approach has been adopted [32].…”

Section: Spectrum Estimation Methodsmentioning

confidence: 99%

Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency

Rossi

Nardone

Settanta

et al. 2023

Sensors

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“…Nevertheless, an entirely endogenous automated mechanism has been introduced in the current setup, which does not require user interference. The presumption for the new mechanism is constructed based on the fact that the wave distribution (Marta Berardengo, 2021) and subsequent responses (Tangirala, 2018) follow a Gaussian distribution in the respective time series. In this order, 𝑌(𝑡) − 239𝑠 amounts to 4 minutes of passive data was selected as the passive window, and a grid was made up by different proportion of standard deviation 𝜎 of signal amplitude within the passive window according to Equation ( 6).…”

Section: 3mentioning

confidence: 99%

“…Wave as the dominant medium is the main exciting force on floating units at sea, where the knowledge of associated parameters has a crucial role to play in increasing operability, enhancing efficiency, and reducing involved operational risks. The range of applications which desperately require seaway data embraces almost all onboard activities, including but not limited to wave energy convertor (Ge & Kerrigan, 2016), ship passage planning, structural health monitoring (Marta Berardengo, 2021), control and stability compensation systems , ship-to-ship operation, aircraft, and helicopter landing, launching missiles and onboard weapons, crane operation (Nielsen, 2017), and last but not least weather forecasting and meteorological pattern (Rossi, et al, 2022). Therefore, given the significance, many tools have been designed to record the wave data throughout the global open seas and oceans, for example, rider buoys, down-looking radars, and weather satellites for direct measurement of the seaway.…”

Section: Introductionmentioning

confidence: 99%

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Majidiyan,

Enshaei,

Howe

et al. 2024

Preprint

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Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. Despite extensive research on the wave buoy analogy (WBA), real-time sea state estimation (SSE) has remained challenging due to the need for adequate data to construct a consistent probability density function of waves. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram form for subsequent data analysis. To achieve this, an adaptive ship response predictor and deep learning approach have been incorporated to classify seaway data while minimizing network complexity through feature engineering. Specifically, a Convolution Neural Network (CNN) and deep transfer learning have been employed for training purposes. The system's performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the proposed approach for a fully automated system. Furthermore, the deficits with the feature transformation of the existing data-driven SSE models have been discussed. This study provides a foundation for improving online sea state estimation and promoting the seaway acquisition for stationary marine units.

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“…et. al, 2017), efficient passage planning, and structural safety and crews' comfort onboard (Marta Berardengo, 2021). Furthermore, in emerging trends such as wave energy convertors, accurate sea state information is crucial for optimizing the system's performance (Ge & Kerrigan, 2016).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sea State Estimation Using Deep Transfer Learning; an Integrated Framework

Majidiyan,

Enshaei,

Howe

2024

Preprint

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This study addresses the crucial need for wave parameter identification in floating units at sea, where waves serve as a primary environmental force. Despite decades of research, online wave estimation using ship-borne sensors, known as the wave buoy analogy (WBA), has not provided a definitive solution for deterministic sea state estimation. Our study proposes an integrated AI system that minimizes the time lag between the excitation wave and wave estimation based on the current state. Our innovative approach involves a threshold self-exciting mechanism, dynamically switching between different predictors based on statistical characteristics within a fixed observation window of ship responses. This mechanism adjusts the buffer window for wave estimation. Time-series responses from a moored semi-submersible simulation undergo preprocessing to extract detailed spectral and temporal features. This data is then transformed into scalograms and fed into a deep-learning network. A novel aspect of our work involves exploring transfer learning and leveraging off-the-shelf networks to accelerate the training process. The present setting not only effectively reduces time lag but also showcases the potential of transfer learning for generalizing sea state estimation models. The promising outcomes of this research, in turn, pave the way for advanced seakeeping capabilities and safer marine operations.

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Sea Spectral Estimation Using ARMA Models

Cited by 15 publications

References 36 publications

Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency

Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Real-Time Sea State Estimation Using Deep Transfer Learning; an Integrated Framework

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