Adaptive identification of lowpass filter cutoff frequency for online vessel model tuning

OCEAN ENGINEERING

卷:236

Article

Han, Xu¹ 

[1] Norwegian Univ Sci & Technol NTNU, Dept Marine Technol, N-7491 Trondheim, Norway

关键词: Adaptive lowpass filtering;    Optimal cutoff frequency;    Wave-induced vessel responses;    On-site measurements;    Online vessel model tuning;    Discrete Bayesian updating;   

DOI  :  10.1016/j.oceaneng.2021.109483

来源: Elsevier

【 摘 要 】

Tuning of vessel models in real-time based on vessel measurements and weather information is of great interest in order to increase the safety and efficiency of marine operations. Vessel motion signals usually contain high frequency noise. For an unbiased model tuning algorithm, it is essential to filter the noisy signals in order to identify the power of the wave-induced first-order vessel response. Lowpass filters with high accuracy should therefore be applied. However, it is a challenge to design such a filter since the optimal cutoff frequency can vary with sea states, vessel dimensions, vessel conditions, etc. This paper proposes a novel algorithm to adaptively search for the optimal cutoff frequency for a lowpass filter with high accuracy. The algorithm is fundamentally based on the facts that the vessel naturally acts as a lowpass filter and the energy from the high-frequency components, e.g., signal noise, is significantly smaller than that from the wave-induced vessel response. The algorithm is validated by 500 numerically simulated vessel motion signals with quite high noise levels and also by analysis of several on-site full-scale vessel motion signals. The improvements to the tuning results for the vessel parameters are demonstrated.

【 授权许可】

Free