Multiparameter-driven gearbox health assessment using hybrid GAN-LSTM networks

Gearboxes represent a key feature of wind turbines, fulfilling the function of connecting and transmitting power. The analysis of gearbox health assessment has the capacity to predict the failure time of the wind turbine with both timeliness and accuracy, thus providing guidance for subsequent maintenance and repair. In this paper, a gearbox health assessment method based on an adversarial network is proposed. The transition from normal to fault conditions facilitates the early detection of faults. The residual between the original signal and the predicted signal is utilised to indicate whether the gearbox operating state is normal. The deviation from the normal data to the abnormal data is quantified through the deviation index and the technique of change. This is then converted into a probability value to obtain the health index of a single variable. The overall health index (HI) of the gearbox is composed of a single variable index. The experimental findings demonstrate the efficacy of the proposed method in conducting health assessments of wind turbine gearboxes, thereby substantiating its wide-ranging applicability.