A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines

Chatterjee, Joyjit; Dethlefs, Nina

Abstract

© 2020 IEEE. Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs. Machine learning has been applied to fault prediction in wind turbines, but these predictions have not been supported with suggestions on how to avert and fix faults. We present a data-to-text generation system utilising transformers for generating corrective maintenance strategies for faults using SCADA data capturing the operational status of turbines. We achieve this in two stages: a first stage identifies faults based on SCADA input features and their relevance. A second stage performs content selection for the language generation task and creates maintenance strategies based on phrase-based natural language templates. Experiments show that our dual transformer model achieves an accuracy of up to 96.75% for alarm prediction and up to 75.35% for its choice of maintenance strategies during content-selection. A qualitative analysis shows that our generated maintenance strategies are promising. We make our human- authored maintenance templates publicly available, and include a brief video explaining our approach.

Citation

Chatterjee, J., & Dethlefs, N. (2020, July). A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 International Joint Conference on Neural Networks (IJCNN)
• Start Date: Jul 19, 2020
• End Date: Jul 24, 2020
• Acceptance Date: Jun 1, 2020
• Online Publication Date: Sep 28, 2020
• Publication Date: 2020
• Deposit Date: Jun 8, 2022
• Publicly Available Date: Sep 29, 2022
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2020 International Joint Conference on Neural Networks (IJCNN)
• ISBN: 9781728169262
• DOI: https://doi.org/10.1109/IJCNN48605.2020.9206839
• Public URL: https://hull-repository.worktribe.com/output/3655240
• Publisher URL: https://ieeexplore.ieee.org/document/9206839