Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

Abstract

Under the framework of normal behavior modeling, this paper develops a novel scheme for fault detection via quantile regression neural networks (QRNNs). The QRNN model is a combination of quantile regressions and neural networks. It is able to identify the normal status or extract the normal behavior data accurately and quickly through lower and upper regression quantiles.Additionally, it is flexible to explore the potential nonlinear patterns contained in the normal status by taking advantage of neural networks. Finally, we monitor the residuals produced from QRNN to detect faults by using the exponentially weighted moving average (EWMA) control chart. The utility of our scheme is illustrated by empirical analyses of bearing fault detection based on Supervisory Control and Data Acquisition (SCADA) data from a wind turbine. We find that the QRNN model outperforms the multiple linear regression (MLR) and back propagation neural networks (BPNNs) in terms of mean absolute error (MAE). Besides, the obtained relationship between the width of control limits in EWMA and the number of alarms provides an important and convenient way for practical applications. KEYWORDS exponentially weighted moving average, fault detection, normal behavior modeling, quantile regression neural networks, wind turbine INTRODUCTIONWind energy is one of the most significant clean renewable energies for electrical generation. An increasing number of wind farms have been built to fulfill human's growing power demand. However, wind turbines (WTs) are typically deployed in a remote or offshore location with abundant wind energy, where performing regular inspections to maintain normal operations will cost a lot. In addition, unscheduled and reactive maintenances could increase the meaningless downtime, causing the loss of revenue. Therefore, how to perform more effective operations and maintenance procedures via monitoring remotely with lower cost has attracted more and more attentions from both academics and practitioners.Condition monitoring systems (CMSs) require not only high-level knowledge about the systems but also the high-cost vibration sensors, which hinders from the prevalence of CMSs. 1-3 On the contrary, the data collected via Supervisory Control and Data Acquisition (SCADA) systems that archive useful information in a convenient manner is universally available. Extensive efforts have been devoted to developing various methods for fault detection of WTs based on SCADA data.Based on SCADA data, the residuals produced from three models including a generalized mapping regressor (GMR), a general regression neural network (GRNN), and a feed-forward multilayer perceptron (MLP) have already been used to monitor the power curve of WTs. 4 The copula approach, estimating bivariate probability distribution function, is proposed to model the power curve so that the deviation between the existing and expected behavior can be checked. 5 The Mahalanobis distance is applied to detect faults in the optional curves containing the power curve, rotor curv...

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