A machine learning approach to comfort assessment for offshore wind farm technicians

Current maintenance planning strategies in the operations and maintenance of offshore wind farms rarely account for the comfort of technicians during transits. This creates uncertainties as transit from the vessel to structure might be unacceptable to technicians. Here, the authors model the welfare of technicians using the discomfort from the motions (three-dimensional accelerations) felt on crew transfer vessels (CTVs) during transits from port to wind farm. To explore technician exposure to vibration, acceleration data from vessel motion monitoring systems deployed on CTVs operating in the North Sea was synchronised with sea-state data from an operational ocean model. Processes of dimensionality reduction and machine learning (ML) were used to model the comfort of technicians from operational limits applied to models predicting Composite Weighted RMS Acceleration. Trained models were shown to provide estimations for the comfort variable with an R² value of 0.67 and an RMSE of 0.06 ms⁻². The comfort-based decision-making model is shown to be able to predict sail or not sail decisions for maintenance transits. The proposed model will have applications in maintenance planning for offshore wind farms, able to account for the comfort of technicians once identified limitations have been addressed to improve model predictions.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2023.114934
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801823013185
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • Crown Copyright © 2023 Published by Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Uzuegbunam, Tobenna D
  • Uzuegbunam, Francis O
  • Ibem, Eziyi O
• Publication Date: 2023-7-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 114934
• Serial:
  • Ocean Engineering
  • Volume: 280
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Crew comfort; Human factors; Machine learning; Maintenance; Offshore structures; Ships; Wind power generation
• Geographic Terms: North Sea
• Subject Areas: Maintenance and Preservation; Marine Transportation; Safety and Human Factors;

Filing Info

• Accession Number: 01887661
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 17 2023 3:13PM