Comparison of anomaly detection techniques for wind turbine gearbox SCADA data
Mckinnon, C. and Carroll, J. and McDonald, A. and Koukoura, S. and Soraghan, C. (2019) Comparison of anomaly detection techniques for wind turbine gearbox SCADA data. In: Wind Energy Science Conference 2019, 2019-06-17 - 2019-06-20, University College Cork.
Preview |
Text.
Filename: McKinnon_etal_WESC_2019_Comparison_of_anomaly_detection_techniques_for_wind_turbine_gearbox_SCADA_data.pdf
Accepted Author Manuscript Download (436kB)| Preview |
Abstract
This analysis looks at the use of anomaly detection to assess the condition of wind turbine gearboxes based on data from a number of operational turbines. A comparison is made between various methods of anomaly detection, these being one class support vector machine (OCSVM), random forests, and nonlinear autoregressive neural networks with exogenous inputs (NARX).
ORCID iDs
Mckinnon, C., Carroll, J.
ORCID: https://orcid.org/0000-0002-1510-1416, McDonald, A. ORCID: https://orcid.org/0000-0002-2238-3589, Koukoura, S. and Soraghan, C.;
-
-
Item type: Conference or Workshop Item(Other) ID code: 70261 Dates: DateEvent17 June 2019PublishedKeywords: anomaly detection, operations and maintenance (O&M), wind turbines, one class support vector machine, neural networks with exogenous inputs, wind energy, Electrical Engineering. Electronics Nuclear Engineering, Energy Engineering and Power Technology Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Engineering > Naval Architecture, Ocean & Marine EngineeringDepositing user: Pure Administrator Date deposited: 24 Oct 2019 10:42 Last modified: 17 Aug 2023 00:44 URI: https://strathprints.strath.ac.uk/id/eprint/70261
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)