Virtual Sensors for Mooring Line Tension Monitoring
- Vivek Jaiswal (DNV GL) | Aaron Austin Brown (DNV GL) | Mengxi Yu (DNV GL)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- 4.5.4 Mooring Systems, 5.5 Reservoir Simulation, 7.6.7 Neural Networks, 7.6.6 Artificial Intelligence, 6.1 HSSE & Social Responsibility Management, 5 Reservoir Desciption & Dynamics, 7 Management and Information, 6 Health, Safety, Security, Environment and Social Responsibility, 7.6 Information Management and Systems, 6.3 Safety, 6.1.5 Human Resources, Competence and Training
- integrity management, mooring tension monitoring, machine learning, virtual sensor
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Mooring line tension monitoring is required for permanently moored floating offshore platforms by some regional regulators and classification societies. This requirement is typically satisfied by installing physical sensors that directly measure the line tension. Experience shows these sensors have relatively short life compared to the platform operational life and consequently they need to be changed several times thereby increasing the operational expenses. It is also possible that changing the sensors in the field may not be feasible due to access and safety issues or it may be prohibitively expensive, which could lead to the platform operating without meeting the regulations.
This paper presents a machine learning based model, which we call ‘virtual sensor’, for predicting the mooring line tensions based on the platform’s heading, horizontal position and six-degrees-of-freedom (6-dof) rigid body motions. The model’s development and testing are demonstrated with the help of data generated through numerical simulations of a permanently moored semi-submersible. When deployed in field, the inputs to the virtual sensor would be obtained from the global position system (GPS) and accelerometers. Both the GPS and accelerometer are cheaper to install and maintain, reliable and easy to replace.
The neural network model is pre-trained using a dataset of 5000 static simulations and further fine-tuned with 48 dynamic simulation cases. Model performance on four mooring lines are presented in the study. The accuracy of the model was assessed by determining the percentage of predictions with errors within ±5% of the simulated mooring line tensions. Three of the mooring lines achieved accuracy greater than 90% and one mooring line achieved 77% accuracy. The relevant limitations of the study and future work are discussed in the paper.
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