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 CO2 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. (in press). Memristor-based LSTM neuromorphic circuits for offshore wind turbine blade fault detection. IEEE International Symposium on Circuits and Systems, 2023, 1-5. https://doi.org/10.1109/ISCAS46773.2023.10181552

• Journal Article Type: Conference Paper
• Conference Name: 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 21-25 May 2023
• Conference Location: Monterey, CA, USA
• Acceptance Date: Mar 1, 2023
• Online Publication Date: Jul 21, 2023
• Deposit Date: Sep 5, 2023
• Publicly Available Date: Sep 8, 2023
• Journal: IEEE International Symposium on Circuits and Systems
• Print ISSN: 0271-4310
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 2023
• Pages: 1-5
• ISBN: 9781665451093
• DOI: https://doi.org/10.1109/ISCAS46773.2023.10181552
• Public URL: https://hull-repository.worktribe.com/output/4367604