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Monitoring the state of wind turbine blades in real-time using sensors is crucial for early fault diagnosis. Several studies have been conducted to predict the failure of wind turbine blades based on data measured by sensors. These methods rely on accuracy of the sensor-monitoring data; even minor abnormalities can lead to misjudgment of the blade condition and cause serious consequences in service. Nevertheless, self-diagnosing schemes for sensor faults are less researched. The data measured by all sensors on the same wind turbine blade constitutes a spatiotemporal joint distribution dataset, which forms a data correlation pattern. Therefore, this paper proposes a sensor fault self-diagnosing scheme that does not depend on any labeled fault data. First, a sensor data prediction model based on deep learning is built by mining the inherent relevance between sensors. Second, a sensor fault is detected when the residual between the measured sensor value and the predicted value exceeds the control limit. The experimental results for a real-world wind turbine blade show that the model has good prediction and fault diagnosis performance.

Deep Learning Approach for Sensor Data Prediction and Sensor Fault Diagnosis in Wind Turbine Blade