Parametric roll resonance monitoring using signal-based detection. (15th November 2015)

• Record Type: Journal Article
• Authors: Galeazzi, Roberto; Blanke, Mogens; Falkenberg, Thomas; Poulsen, Niels K.; Violaris, Nikos; Storhaug, Gaute; Huss, Mikael
• Is Part Of: Ocean engineering. Volume 109 (2015)
• Page Start: 355
• Page End: 371
• Publication Date: 2015-11-15
• Subjects: Parametric roll resonance -- Statistical change detection -- Ship stability -- Full-scale validation; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2015.08.037
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Extreme roll motion of ships can be caused by several phenomena, one of which is parametric roll resonance. Several incidents occurred unexpectedly around the millennium and caused vast fiscal losses on large container vessels. The phenomenon is now well understood and some consider parametric roll a curiosity, others have concerns. This study employs novel signal-based detection algorithms to analyse logged motion data from a container vessel (2800 TEU) and a large car and truck carrier during one year at sea. The scope of the study is to assess the performance and robustness of the detection algorithms in real conditions, and to evaluate the frequency of parametric roll events on the selected vessels. Detection performance is scrutinized through the validation of the detected events using owners' standard methods, and supported by available wave radar data. Further, a bivariate statistical analysis of the outcome of the signal-based detectors is performed to assess the real life false alarm probability. It is shown that detection robustness and very low false warning rates are obtained. The study concludes that small parametric roll events are occurring, and that the proposed signal-based monitoring system is a simple and effective mean to provide timely warning of resonance conditions. Abstract : Highlights: Monitoring system of parametric roll validated against full scale motion data. Robustness assessed through bivariate statistical analysis using copula descriptors. Probability of false alarms drastically reduced by the combined hypothesis testing. Successful full scale validation on data sets with parametric roll events. Successful full scale validation on a blind test with 1 year of data from a LCTC. Results of the full scale validation acknowledged by industrial partners.