Sensors for Wind Turbine Fault Diagnosis and Prognosis

Dear Colleagues,

To remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. Thus, it is essential to develop robust and cost-effective prognostic and health management strategies.

On the one hand, wind turbines generate a wealth of SCADA data from a variety of sensors, which can be effectively used to enable fault diagnosis and prognosis strategies. Data-driven techniques, based on machine or deep learning, are particularly promising in this field. Furthermore, this approach is cost-efficient and readily available as no extra equipment needs to be installed in the wind turbine. However, managing this large amount of data is a challenge as SCADA data is low-sampled data (10-min averaged data), gathered under a variety of operational modes and environmental conditions, and always subject to an external unknown excitation, the wind.

On the other hand, accurate prognosis and diagnosis of WT failures could rely on purpose-built condition monitoring (CM) systems. Vibration-based condition monitoring is a well-established strategy but it usually relies on high-sampled data (>10 kHz) leading to a large amount of data from a large number of sensors. Furthermore, for a CM system, the accuracy of data acquired from sensors has a pronounced impact on performance. Finding patterns in such multivariable datasets is a challenge under the aforementioned variety of operational modes and environmental conditions that wind turbines are subject to.

This Special Issue invites contributions that address wind turbine fault prognosis and diagnosis. In particular, submitted papers should clearly show novel contributions and innovative applications covering, but not limited to, any of the following topics around wind turbines:

Dr. Yolanda Vidal
Guest Editor

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