Power transformers

as essential equipment for electricity transmission

may fail due to insulation degradation. Predicting the failure rate of power transformers precisely is beneficial to decision-making. Currently

uncertainties of the prediction have not been deeply discussed. Besides

prediction accuracy is not high enough. This paper proposes a decomposition-based Bayesian deep learning (BDL) method to predict the failure rate of power transformers. Both the model uncertainty related to distribution of the model's weights and the inherent uncertainty associated with random noise can be captured by BDL. Uncertainties of prediction results are depicted with confidence intervals. Moreover

prediction accuracy is improved using variational mode decomposition (VMD). Numerical experiments have been carried out based on oil chromatographic data of power transformers from the Chongqing grid to validate effectiveness of the proposed method.