Memristor-based LSTM neuromorphic circuits for offshore wind turbine blade fault detection

Burton, Harry; Bouillard, Jean Sebastien; Kemp, Neil

Abstract

The UK offshore wind industry is rapidly growing to meet CO 2 emission targets. However, the main drawback of the offshore environment is the increased cost of maintenance. Artificial Neural Networks (ANN) show great potential to reduce this cost. Long Short-Term Memory (LSTM) is a form of Recurrent Neural Network (RNN) that shows promising results in solving time series-based problems, making them ideally suited for wind turbine condition monitoring. A dedicated circuit for a LSTM-based ANN that uses memristors will allow for more power efficient and faster computation, whilst being able to overcome the von Neumann bottleneck.

Citation

Burton, H., Bouillard, J. S., & Kemp, N. (2023, May). Memristor-based LSTM neuromorphic circuits for offshore wind turbine blade fault detection. Presented at 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, USA

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2023 IEEE International Symposium on Circuits and Systems (ISCAS)
• Start Date: May 21, 2023
• End Date: May 25, 2023
• Acceptance Date: May 21, 2023
• Online Publication Date: Jul 21, 2023
• Publication Date: May 21, 2023
• Deposit Date: Aug 2, 2023
• Publicly Available Date: Aug 3, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 2699-2703
• Series Title: IEEE International Symposium on Circuits and Systems
• Series ISSN: 2158-1525
• Book Title: 2023 IEEE International Symposium on Circuits and Systems (ISCAS)
• ISBN: 978-1-6654-5110-9
• DOI: https://doi.org/10.1109/iscas46773.2023.10181552
• Public URL: https://nottingham-repository.worktribe.com/output/23738472
• Publisher URL: https://ieeexplore.ieee.org/document/10181552