Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures

This paper investigates the utilization of artificial neural networks (ANNs) for the proactive identification of gearbox failures in wind turbines, boosting the use of operational SCADA data for predictive analysis. Avoiding gearbox failures, which can strongly impact the functioning of wind turbines, is crucial for ensuring high reliability and efficiency within wind farms. Early detection can be achieved though the development of a normal behavior model based on ANNs, which are trained with data from healthy conditions derived from selected SCADA variables that are closely associated with gearbox operations. The objective of this model is to forecast deviations in the gear bearing temperature, which serve as an early warning alert for potential failures. The research employs extensive SCADA data collected from January 2018 to February 2022 from a wind farm with multiple turbines. The study guarantees the robustness of the model through a thorough data cleaning process, normalization, and splitting into training, validation, and testing sets. The findings reveal that the model is able to effectively identify anomalies in gear bearing temperatures several months prior to failure, outperforming simple data processing methods, thereby offering a significant lead time for maintenance actions. This early detection capability is highlighted by a case study involving a gearbox failure in one of the turbines, where the proposed ANN model detected the issue months ahead of the actual failure. The present paper is an extended version of the work presented at the 5th International Conference of IFToMM ITALY 2024.