Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Damage Identification of Mooring System for Offshore Structure with Automated Machine Learning
Ping Li, Chaohe Chen*

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China.
fields(Davies, 2021).
A number of scholars have also conducted research on this topic inacademia. Jaiswal (2019) discussed a new method to detect mooringline failure using measured ship position and 6-degree-of-freedom
acceleration and deep learning algorithms. The result of numerical casestudy of Floating Production Storage and Offloading(FPSO) by
Convolutional Neural Networks (CNN) showed that the image
recognition method can accurately identify damage to more than 99%of the mooring system. Gumley (2016) proposed a novel method tocontinuously track and predict the performance of moored floats undervarious ocean conditions and use this capability to quickly detectchanges in the performance of a particular FPSO mooring system.
Prislin (2017) investigated the use of an artificial neural network (ANN)model to identify mooring cable failures. Preliminary results based on
simulated data showed that the accuracy of this prediction was better
than 98%. Also, the paper finds that this method can be deployed on all
types of floating platforms at relatively moderate capital costs and very
low operating costs compared to the high capital and operating costs ofmooring cable monitoring systems.
The above research showed that machine learning can judge whether
the mooring line is broken in the safety detection of mooring line.
However, we argue that there are some limitations of these methods, as
it takes a lot of time to process image data when using CNN forclassification recognition, and the training time of image recognition ismuch slower tabular data. Also, for researchers in the field of oceanengineering, it is not our scope to study machine learning acrossdisciplines.If there is a simple framework that can automaticallyprocess the algorithm and model parameters of machine learning, we
can directly apply them to our respective professional fields and getgood prediction results. This will be a great progress and expansion for
ocean engineering and machine learning. Therefore, this paper focuseson the application of AutoML in marine engineering, where developers
can write AI-embedded applications relying on just a few lines of code.
The AI algorithm is learned by the machine itself to identify thecondition of mooring lines by feature extraction and damage
identification with six degrees of freedom motion response of a drillingsemi-submersible under different sea conditions. This method canprovide safety assessment information for marine structures and hassome practical value.

ABSTRACT
Marine structural mooring systems can suffer a lot of damage during
their service life, and if not detected in time, these damages can lead toserious consequences. Traditionally, damage identification in mooring
systems has focused on measuring the tension of mooring lines.
However, physical tension sensors are difficult and costly to maintain.
In recent years, machine learning and deep learning methods havedeveloped rapidly, and if these more accurate, lower cost, and smarter
methods can be applied to the field of marine engineering, they will
greatly improve the efficiency and accuracy of mooring system damage
identification. In this paper, based on the Autogluon framework in
automated machine learning (AutoML), we determine whether themooring system of drilling semi-submersible is damaged by feature
extraction and damage identification of the six-degree-of-freedom
motion response under different sea conditions. The method canprovide safety assessment information for marine structures and hassome practical value.
KEY WORDS:
AutoGluon.

Mooringsystem;damage

detection;

AutoML;INTRODUCTION
With the gradual development of the field of offshore engineering todeep offshore areas, the research and application of floating structures
are increasing. In the mooring system of offshore engineering, theconventional method to monitor the breakage or not of mooring cable is
to arrange a certain number of sensors to evaluate whether the mooring
cable is intact or not by measuring the physical tension. However, theaccuracy and reliability of this method are often not guaranteed. KaiTung Ma(2013), of Chevron Energy Technology Co. Ltd, said that
there were 21 mooring failures between 2001 and 2011, with anaverage of more than two per year. Nine of them were multiple-line
failures. Maslin(2013) estimated that 150 mooring lines were repairedor replaced during that period, which had integrity issues. The sensors
used to monitor physical tension on mooring lines are expensive anddifficult to maintain. According to DNV(2019), field experience showsthat they are prone to failure within a few years after installation.
As a result, there are limitations in the traditional methods of detecting
and predicting mooring line failures. To ensure the safety of people andproperty on offshore structures, it is necessary to develop new detection
technology. In recent years, it is an important research idea to combine
machine learning algorithm in artificial intelligence in different

MOORING DAMAGE DETECTION METHOD
The breakage of one (or more) mooring lines can lead to changes in thestability of the floating structure. Therefore, the motion response of thefloating body in 6 degrees of freedom is different in similar marine

1922