An artificial neural network based condition monitoring method for wind turbines, with application to the monitoring of the gearbox
Journal article, 2017

Major failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition-based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA-based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post-processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method.


Pramod Bangalore

Chalmers, Energy and Environment, Electric Power Engineering

Simon Letzgus

University of Stuttgart

Daniel Karlsson

Chalmers, Energy and Environment, Electric Power Engineering

Michael Patriksson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Swedish Wind Power Technology Center (SWPTC)

Wind Energy

1095-4244 (ISSN) 1099-1824 (eISSN)

Vol. 20 8 1421-1438

Driving Forces

Sustainable development

Subject Categories

Computational Mathematics

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

Areas of Advance



Basic sciences



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