Yang, B., Liu, R. and Chen, X. (2018) Sparse Time-Frequency Representation for Incipient Fault Diagnosis of Wind Turbine Drive Train. IEEE Transactions on Instrumentation and Measurement, 67, 2616-2627.
https://doi.org/10.1109/TIM.2018.2828739

has been cited by the following article:

• 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.