Developing a deep learning model for wind turbine bearing fault classification using acoustic signals.
By applying structural analysis to a generic model of a wind turbine hydraulic pitch system, this paper systematically assesses the system's inherent capabilities for detecting and isolating faults, revealing that while most faults are isolable with standard sensor configurations, friction increases in the cylinder are undetectable due to the model structure.