使用机器学习进行系泊线故障检测

V. Jaiswal, A. Ruskin
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引用次数: 9

摘要

海上浮船系泊故障和随后的站点丢失可能对船舶和相关的海底基础设施造成灾难性后果。因此,完整性管理和及时发现系泊故障至关重要。传统的故障检测方法依赖于线张力测量和观察圈方法。这两种方法都有局限性,而且不可靠。因此,需要检测线路故障的替代方法。本文讨论了一种利用测量船舶位置和6自由度加速度以及深度机器学习算法来实时检测系泊线故障的新方法。对一艘转塔系泊FPSO的4000多个测试案例进行了数值研究,结果表明,该方法可以在99%的情况下准确识别出失效的系泊线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mooring Line Failure Detection Using Machine Learning
Offshore floating vessel mooring failure and subsequent loss of station can have catastrophic consequences for the vessel and the associated subsea infrastructure. Therefore, integrity management and timely detection of mooring failure is critical. Traditional methods of failure detection rely on line tension measurements and watch circle approaches. Both these approaches have limitations and are not reliable. Alternate methods of detecting line failure are therefore required. This paper discusses a novel approach of using measured vessel positions and 6-degrees-of-freedom accelerations along with a deep machine learning algorithm to detect mooring line failure in near real time. Results from a numerical case study for a turret moored FPSO with over 4000 test cases demonstrate that this approach can accurately identify failed mooring line cases over 99% of the time.
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