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Quantum-inspired LSTM-Transformer paper published in Energy Conversion and Management: X
Our unsupervised condition monitoring framework combines a quantum LSTM with a Transformer and a Bayesian sliding-window threshold, validated on real SCADA data with gearbox bearing, generator winding and pitch system faults.
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QLSTM fault detection presented at ICAC 2025
Zhefeng Zhang presented “Wind Turbine Fault Detection Using Quantum Long-Short Term Memory Network” at the 30th International Conference on Automation and Computing.
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Review of quantum machine learning for wind turbine condition monitoring published
Our review in Energy Conversion and Management surveys quantum machine learning methods for wind turbine condition monitoring and sets out future research directions.
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