Uncertainty-Aware Remaining Useful Life Prediction of Power Transformers Using PatchTST and Deep Evidential Regression

Remaining useful life (RUL) prediction of power transformers is essential for ensuring reliable operation and supporting condition-based maintenance. However, the complex nonlinear degradation characteristics and long-term temporal dependencies of transformer monitoring data pose significant challenges for accurate RUL prediction. Moreover, most existing deep learning-based prediction methods provide deterministic predictions without explicitly quantifying predictive uncertainty, while the consequences of RUL overestimation and underestimation in practical maintenance are inherently asymmetric. Therefore, this paper proposes an uncertainty-aware RUL prediction method for power transformers by integrating the Patch Time Series Transformer (PatchTST) with deep evidential regression (ER-PatchTST). PatchTST is used to transform long time-series inputs into patch-level representations, enabling the model to capture local temporal patterns and long-range dependencies through patch-wise tokenization and channel-independent modeling. In addition, the deep evidential regression module is designed by placing a Normal–Inverse-Gamma (NIG) prior over the parameters of the Gaussian likelihood, which can simultaneously predict RUL and quantify aleatoric and epistemic uncertainties in a single forward pass. Furthermore, a safety-oriented RUL indicator and hierarchical warning strategy are developed, and different maintenance actions are initiated when alarms at different levels are triggered. Experiments on the ETT dataset demonstrate that ER-PatchTST achieves competitive forecasting performance compared with state-of-the-art time-series forecasting methods while simultaneously providing predictive uncertainty estimates. On the DGA dataset, ER-PatchTST achieves the best RUL prediction performance among the compared methods and provides informative uncertainty quantification for condition-based maintenance decision support.