Leite, G.D.N.P., da Cunha, G.T.M., dos Santos Junior, J.G., Araújo, A.M., Rosas, P. A.C., Stosic, T., Stosic, B. and Rosso, O.A. (2021) Alternative Fault Detection and Diagnostic Using Information Theory Quantifiers Based on Vibration Time-Waveforms from Condition Monitoring Systems: Application to Operational Wind Turbines. Renewable Energy, 164, 1183-1194.
https://doi.org/10.1016/j.renene.2020.10.129

has been cited by the following article:

• TITLE: SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

  AUTHORS: Javier Vives, Juan Palací, Janverly Heart

  KEYWORDS: Vibrations, Wind Turbines, Fault Diagnosis, Machine Learning, Condition Monitoring, Deep Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.11, November 9, 2022

  ABSTRACT: With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine’s electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.