Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection

Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection

Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum...

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Bibliographic Details
Main Authors: Cofre‐martel, S. (Author), Correa‐jullian, C. (Author), Costa, A. (Author), Droguett, E.L (Author), Leite, G.N.P (Author), Martin, G.S (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Auto encoders
autoencoders
Decision trees
Deep learning
Fault detection
Faults detection
Faults diagnostics
feature reduction
Features reductions
machine learning
Pitch fault diagnostic
pitch fault diagnostics
principal component analysis
Principal component analysis
Principal-component analysis
Prognostic and health management
prognostics and health management
Quantum computers
Quantum kernel
quantum kernels
quantum machine learning
Quantum machine learning
SCADA system
SCADA systems
Support vector machines
Systems engineering
wind turbine systems
Wind turbine systems
Wind turbines
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