Effective Random Forest-Based Fault Detection and Diagnosis for Wind Energy Conversion Systems Academic Article

authors

• Nounou, Mohamed

published proceedings

• IEEE SENSORS JOURNAL

author list (cited authors)

• Fezai, R., Dhibi, K., Mansouri, M., Trabelsi, M., Hajji, M., Bouzrara, K., Nounou, H., & Nounou, M.

citation count

• 31

complete list of authors

• Fezai, Radhia||Dhibi, Khaled||Mansouri, Majdi||Trabelsi, Mohamed||Hajji, Mansour||Bouzrara, Kais||Nounou, Hazem||Nounou, Mohamed

publication date

• January 2021

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Computational Modeling
• Fault Detection
• Fault Detection And Diagnosis
• Feature Extraction
• Hierarchical K-means (h-kmeans)
• Kernel
• Kernel Principal Component Analysis (kpca)
• Principal Component Analysis
• Radio Frequency
• Random Forest (rf)
• Reduced Kpca
• Vegetation
• Wind Energy Conversion Systems

Digital Object Identifier (DOI)

• 10.1109/JSEN.2020.3037237

start page

• 6914

end page

• 6921

volume

• 21

issue

• 5

URL

• http://dx.doi.org/10.1109/jsen.2020.3037237

user-defined tag

• 7 Affordable and Clean Energy