Integrated data‐driven model‐based approach to condition monitoring of the wind turbine gearbox.

Condition monitoring (CM) is considered an effective method to improve the reliability of wind turbines (WTs) and implement cost‐effective maintenance. This study presents a single hidden‐layer feedforward neural network (SLFN), trained using an extreme learning machine (ELM) algorithm, for CM of WTs. Gradient‐based algorithms are commonly used to train SLFNs; however, these algorithms are slow and may become trapped in local optima. The use of an ELM algorithm can dramatically reduce learning time and overcome issues associated with local optima. In this study, the ELM model is optimised using a genetic algorithm. The residual signal obtained by comparing the model and actual output is analysed using the Mahalanobis distance (MD) measure due to its ability to capture correlations among multiple variables. An accumulated MD value, obtained from a range of components, is used to evaluate the health of a gearbox, one of the critical subsystems of a WT. Models have been identified from supervisory control and data acquisition (SCADA) data obtained from a working wind farm. The results show that the proposed training method is considerably faster than traditional techniques, and the proposed method can efficiently identify faults and the health condition of the gearbox in WTs. [ABSTRACT FROM AUTHOR]