Detection of Parametric Roll Resonance using Bayesian Discrete-Frequency Model Selection⁎. Issue 29 (2018)

• Record Type: Journal Article
• Authors: Kennedy, Justin M.; Ford, Jason J.; Perez, Tristan; Valentinis, Francis
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 29(2018)
• Page Start: 444
• Page End: 449
• Publication Date: 2018
• Subjects: Parametric Roll -- Detection Algorithms -- Frequency Estimation -- Bayesian Inference -- Statistical Inference; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.447
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper explores the use of two discrete-frequency models and a probabilistic Bayesian model selection procedure to detect the inception of parametric resonance in ships. We exploit knowledge of the coupling between roll and pitch due to the restoring forces arising from the shape of the hull to propose a single and a double discrete-frequency model. These models are then used within a Bayesian framework to compute the posterior distribution of the frequencies and amplitudes of the two models and this information is used for model selection. The latter is based on the computation of the probability that each one of the models is correct given the data analysed within a past window of samples. The algorithm is tested with data from a scale-model experiment for both regular and irregular sea states. The results indicate the proposed detector is effective in accusing the inception of parametric resonance.