Abstract

Monitoring the integrity of mooring lines on floating offshore platforms is one of the key factors in ensuring safe and productive offshore operations. Sensors, such as inclinometers, compressive load cells or strain sensors, can be used to monitor the inclination angles or tensions on mooring lines. An alternative method using only dry monitoring systems, such as DGPS (Differential Global Positioning System), Gyrocompass and/or IMU (Inertial Measurement / Motion Unit), can also be used to monitor the integrity of mooring lines. This method uses the measured motions and positions of a vessel without any information on the environmental conditions to detect mooring line failure. The detection of mooring line failure is based on detecting shifts in low-frequency periods and mean yaw angles as a function of vessel position, mass and added mass. The proposed method utilizes Artificial Neural Network (ANN) to recognize and classify patterns.

The training of an ANN model requires examples/data associated with intact mooring lines and broken mooring line(s). Examples/data of broken mooring line(s) are practically available only from numerical simulations. Therefore, it is important to address these two key topics: (1) Is the real behavior of the floating offshore platform sufficiently aligned with numerical simulations? and (2) The effect of the accuracy of monitoring equipment on the performance of an ANN-based system.

The first topic is reviewed briefly with its possible solution including some sensitivity tests, and this paper focuses on addressing the second topic. A system architecture is discussed in this paper along with the accuracy of the monitoring equipment. As an example, an ANN model has been trained to detect a broken mooring line of a spread-moored FPSO. This ANN model has been tested on its performance in dealing with a range of possible errors associated with the monitoring equipment. Furthermore, the tests have been carried out for a combination of variables that are not included in the ANN training, such as: vessel draft (mass), sea state conditions and directions. This paper presents the results of the tests for various variable sensitivities, which cover vessel positions, mean yaw angles and vessel drafts. These are essentially testing the tolerance of a trained ANN model against error or noise in the data. The results show that a trained ANN model can be error/noise tolerant.

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