It remains highly challenging to extract features from monitoring signals of wind turbine components under varying operating conditions and complex structures, as well as to quantify the uncertainty of Remaining Useful Life (RUL) predictions. To address these issues, this paper proposes a method called Temporal Convolutional Variational Deep Gaussian Process (TCVDGP). This method introduces Deep Gaussian Processes (DGP) to model the nonlinear relationship between input features and RUL, and employs variational inference to efficiently estimate the posterior distribution for accurate uncertainty quantification. In addition, by integrating Temporal Convolutional Networks (TCN) and a Sequential Attention Mechanism (SAM), the model captures both short- and long-term dependencies in time series data, enhancing adaptability to complex and variable operating conditions. TCVDGP ultimately produces RUL predictions with confidence intervals. Experiments conducted on planetary gearbox and industrial wind turbine gearbox datasets, combined with correlation analysis between the model’s hidden layer outputs and internal gearbox parameters, reveal the reasons behind performance differences of TCVDGP across different degradation stages. The results show that TCVDGP not only effectively captures the health evolution of wind turbine gearboxes but also dynamically adjusts to key variables, significantly improving the reliability and interpretability of RUL predictions under complex operating conditions.
Cui et al. (Fri,) studied this question.