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

Abstract

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.

Sistemas De Monitoramento De Condição Em Usinas Eólicas

“…Os modelos normais de comportamento para as temperaturas do óleo de lubrificação e de rolamento da gearbox são utilizados para obter a indicação de sua condição. A análise pode ser útil para avaliar a qualidade e o desempenho do próprio lubrificante, ou para caracterizar a degradação das peças rotativas através da detecção de partículas suspensas no óleo (BANGALORE et al, 2017;MÉRIGAUD;RINGWOOD, 2016 …”

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

“…The gearbox has one of the highest downtimes and replacement costs out of all wind turbine components . Consequently, trying to predict gearbox failure has been the focus of a number of past papers . Amongst techniques to predict gearbox failure, there are both model‐based and data‐driven approaches.…”

“…The quality of the failure prediction will depend on the complexity and accuracy of the model, which may be adjusted by data‐driven components . Data‐driven approaches are typically based on supervisory control and data acquisition (SCADA) measurements and related inputs …”

“…Data‐driven methods are typically trained to extract features from data and execute a classification by machine learning techniques such as artificial neural networks (ANNs) . Since often a significant number of parameters have to be adjusted, the size of the data set available for training is important.…”

Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach

“…Furthermore, the capability of NN as means to monitor normal behaviour models for condition monitoring of wind turbines can be further enhanced updating preprocessing and post-processing steps, and so improving their training and to reducing their false alarms [14].…”