Deep reinforcement learning for maintenance planning of offshore vessel transfer

Chatterjee, Joyjit; Dethlefs, Nina

Abstract

Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and cost-effective manner. The present optimization of offshore vessel transfer uses mathematical models rather than learning decisions from historical data. In this paper, we design a simulated environment for an offshore wind farm based on Supervisory Control & Acquisition (SCADA) data and alarm logs of historical faults in an operational turbine. Firstly, we utilise a state-of-art decision tree model to predict fault types using SCADA features, and provide explainable decisions. Next, we apply deep reinforcement learning to automatically learn maintenance priorities corresponding to different fault types for ensuring prioritized vessel transfers for critical conditions, and deciding on optimal vessel fleet size. This can lead to significant savings in maintenance costs for the offshore wind industry.

Citation

Chatterjee, J., & Dethlefs, N. (2020). Deep reinforcement learning for maintenance planning of offshore vessel transfer. In C. Guedes Soares (Ed.), Developments in Renewable Energies Offshore Proceedings of the 4th International Conference on Renewable Energies Offshore (RENEW 2020, 12 - 15 October 2020, Lisbon, Portugal) (435-443)

• Conference Name: 4th International Conference on Renewable Energies Offshore (RENEW 2020)
• Conference Location: Lisbon, Portugal
• Start Date: Oct 12, 2020
• End Date: Oct 15, 2020
• Acceptance Date: Oct 1, 2020
• Online Publication Date: Oct 13, 2020
• Publication Date: 2020
• Deposit Date: Jul 11, 2022
• Publisher: Taylor & Francis (Routledge)
• Pages: 435-443
• Book Title: Developments in Renewable Energies Offshore Proceedings of the 4th International Conference on Renewable Energies Offshore (RENEW 2020, 12 - 15 October 2020, Lisbon, Portugal)
• ISBN: 9780367681319
• Public URL: https://hull-repository.worktribe.com/output/3865486
• Publisher URL: https://www.routledge.com/Developments-in-Renewable-Energies-Offshore-Proceedings-of-the-4th-International/Carlos/p/book/9780367681319