Studies from Basque Center for Applied Mathematics Further Understanding of Networks (Condition Monitoring of Mooring Systems for Floating Offshore Wind Turbines Using Convolutional Neural Network Framework Coupled With Autoregressive ...)

Our news journalists obtained a quote from the research from Basque Center for Applied Mathematics, “The precision of CNNs relies on the extraction of meaningful features from input data, necessitating meticulous data curation and labeling for optimal computational efficiency and accurate estimation. Emphasis is placed on the preference for featurerich small datasets over voluminous yet sparse datasets, aiming to enable CNNs to discern crucial patterns more effectively and mitigate issues such as overfitting and extensive preprocessing. The novelty of the proposed approach lies in the integration of AR models, which serve to compress data and enhance damage-sensitive characteristics in the input for CNNs. This integration involves deploying regression models fitted to historical responses, parameterized with AR coefficients sensitive to damage, and further classifying severity using CNNs. The sequential nature of this approach addresses challenges such as vanishing/exploding gradients, particularly for extended historical data, while also attenuating the impact of noise and irrelevant information through data compression. The study explores the effectiveness of the coupled AR -CNN method in monitoring FOWT mooring lines, with a specific focus on two levels of damage identification: detection with classification and damage severity across diverse damage and operational scenarios. The modified methodology exhibits superior outcomes by conducting a performance analysis against traditional CNNs and other machine-learning methods, highlighting the potential of the AR -CNN strategy to improve the precision of FOWT mooring line condition monitoring.”

According to the news editors, the research concluded: “These findings underscore the AR -CNN strategy’s potential to enhance the accuracy of FOWT mooring line condition monitoring.”

This research has been peer-reviewed.

For more information on this research see: Condition Monitoring of Mooring Systems for Floating Offshore Wind Turbines Using Convolutional Neural Network Framework Coupled With Autoregressive Coefficients. Ocean Engineering, 2024;302. Ocean Engineering can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Ocean Engineering - http://www.journals.elsevier.com/ocean-engineering/)

The news correspondents report that additional information may be obtained from Smriti Sharma, Basque Center for Applied Mathematics, Alameda Mazarredo 14, Bilbao 48009, Spain.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.oceaneng.2024.117650. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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