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

机译：基于人工神经网络的风机状态监测方法及其在变速箱监测中的应用

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摘要

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. Copyright (C) 2017 John Wiley & Sons, Ltd.
机译：风力涡轮机的重大故障维修成本高昂，并且由于长时间停机而导致收益损失。由于可以在风力涡轮机中成功应用各种状态监控系统，因此可以降低维护成本的基于状态的维护已成为可能。不断开发改善状态监视系统的新方法。基于存储在风力涡轮机的监督控制和数据采集（SCADA）系统中的数据的监视近来受到关注。事实证明，人工神经网络（ANN）是基于SCADA的状态监视应用程序的强大工具。本文首先概述了最重要的出版物，这些出版物讨论了ANN在风机状态监测中的应用。这些出版物中的知识得到利用和发展，重点放在两个领域：数据预处理和数据后处理。提出了用于数据过滤的方法，这些方法确保了在代表风力涡轮机真实正常运行条件的数据上训练了ANN模型。提出了一种克服由于SCADA数据不连续而导致的ANN模型错误的方法。此外，提出了一种利用马氏距离的方法，该方法通过考虑ANN模型误差与操作条件之间的相关性来改善异常检测。最后，将所提出的方法应用于风轮机齿轮箱故障案例研究。应用结果说明了该方法的优点和局限性。版权所有（C）2017 John Wiley＆Sons，Ltd.

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