Abstract
The SA Agulhas II is a polar supply and research vessel, which operates in the stormy Southern ocean and icy Antarctic waters. She is predisposed to problematic stern slamming. Twelve accelerometer channels from a full-scale vibration measurement system were used to capture measurements of wave incidence on the main deck close to the water line. An automated slamming detection and classification algorithm is proposed to extract slamming incidents from expansive full-scale data. This method is based on image processing of scalograms which are produced by continuous transforms, using the Morlet wavelet. Slams are classified based on the time of incidence. It is shown that high crest factor stern slamming occurs as often as twice per minute.
Highlights
The automatic detection of wave slamming from acceleration measurements on a polar supply and research vessel is investigated
On the SA Agulhas II (SAAII), stern slamming is problematic as it inhibits oceanographic research operations [5]
Omer and Bekker [5] showed that, owing to its impulsive nature, wave slamming could be identified by the broadband excitation patterns in acceleration spectrograms close to the wave impact site
Summary
The automatic detection of wave slamming from acceleration measurements on a polar supply and research vessel is investigated. Slamming is the exposure of a vessel structure to large forces due to wave impacts for a short duration of time [1]. Omer and Bekker [5] showed that, owing to its impulsive nature, wave slamming could be identified by the broadband excitation patterns in acceleration spectrograms close to the wave impact site. The authors highlighted that the segregation of bow and stern slams remains an importiant topic for future investigation. Against this backdrop, an automatic slamming detection algorithm is proposed towards the detection and classification of impulsive wave slamming events. It is envisioned that this algorithm will function within a full-scale monitoring system [6] which sets the requirement of computational efficiency towards real-time analysis
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