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Real-time estimation of riser's deformed shape using inclinometers and Extended Kalman Filter

机译：使用倾斜器和扩展卡尔曼滤波器的立管变形形状的实时估计

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摘要

The real-time monitoring of underwater risers, cables, and mooring lines by multiple sensors is in great demand but still very challenging. In this study, a new real-time riser monitoring method based on an Extended Kalman Filter (EKF) is proposed. It estimates the overall shape of riser in real-time utilizing the measured signals from multiple bi-axial (inclination and heading) inclinometers along the riser. The novel EKF algorithm is shown to be robust against sensor noises and successfully reproduces the actual riser profiles at each time step, which has been verified by multiple tests through numerical simulations. For verification, a turret-moored FPSO (Floating Production Storage and Offloading) with a SCR (Steel Catenary Riser) is employed in four different random waves and currents. Subsequent algorithms are also developed so that the corresponding bending and axial stresses along the riser can also be estimated in real time from the obtained riser shape, which can further be used for the real-time estimation of fatigue-damage accumulation.
机译：多种传感器的水下提升管，电缆和系泊线的实时监测具有很大的需求，但仍然非常具有挑战性。在本研究中，提出了一种基于扩展卡尔曼滤波器（EKF）的新的实时提升机监测方法。它估计利用来自提升管的多个双轴（倾斜和标题）倾角仪的测量信号实时提升机的整体形状。新颖的EKF算法显示为对传感器噪声的稳健，并在每个时间步骤成功再现实际提升器配置文件，通过数值模拟通过多次测试进行了验证。有关验证，在四种不同的随机波和电流中采用带有SCR（钢结网提升管）的炮塔系泊FPSO（浮动生产储存和卸载）。还开发了随后的算法，使得沿着提升管的相应弯曲和轴向应力也可以从所获得的提升管形状实时估计，这可以进一步用于疲劳损坏累积的实时估计。

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