TITLE:
Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm
AUTHORS:
Javier Vives
KEYWORDS:
Wind Turbines, Vibrations, Fault Diagnosis, Machine Learning, Condition Monitoring, Internet of Things
JOURNAL NAME:
Journal of Computer and Communications,
Vol.10 No.7,
July
8,
2022
ABSTRACT: Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.