Comparison of anomaly detection techniques for wind turbine gearbox SCADA data

C. Mckinnon; J. Carroll; A. McDonald; S. Koukoura; C. Soraghan

Comparison of anomaly detection techniques for wind turbine gearbox SCADA data

C. Mckinnon, J. Carroll, A. McDonald, S. Koukoura, C. Soraghan

Research output: Contribution to conference › Abstract

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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).

Original language	English
Number of pages	1
Publication status	Published - 17 Jun 2019
Event	Wind Energy Science Conference 2019 - University College Cork, Cork, Ireland Duration: 17 Jun 2019 → 20 Jun 2019 Conference number: 2019 https://www.wesc2019.org/

Conference

Conference	Wind Energy Science Conference 2019
Abbreviated title	WESC
Country	Ireland
City	Cork
Period	17/06/19 → 20/06/19
Internet address	https://www.wesc2019.org/

Keywords

anomaly detection
operations and maintenance (O&M)
wind turbines
one class support vector machine
neural networks with exogenous inputs
wind energy

Access to Document

McKinnon-etal-WESC-2019-Comparison-of-anomaly-detection-techniques-for-wind-turbine-gearbox-SCADA-dataAccepted author manuscript, 426 KB

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Cite this

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title = "Comparison of anomaly detection techniques for wind turbine gearbox SCADA data",

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). ",

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year = "2019",

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AU - Mckinnon, C.

AU - Carroll, J.

AU - McDonald, A.

AU - Koukoura, S.

AU - Soraghan, C.

N1 - Conference code: 2019

PY - 2019/6/17

Y1 - 2019/6/17

N2 - 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).

AB - 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).

KW - anomaly detection

KW - operations and maintenance (O&M)

KW - wind turbines

KW - one class support vector machine

KW - neural networks with exogenous inputs

KW - wind energy

M3 - Abstract

T2 - Wind Energy Science Conference 2019

Y2 - 17 June 2019 through 20 June 2019

ER -