Deep Learning-Enabled Statistical Model Estimation for Power Transformers with Censoring and Truncation Problems

Abstract

Traditional statistical models, e.g., Weibull distributions, are popular solutions for failure modeling and degradation anal- ysis in a variety of industries. To estimate the parameters of these statistical models, maximum likelihood estimation (MLE) is often engaged through various optimization algo- rithms. However, when dealing with highly reliable or new equipment, it is challenging to fit limited or unbalanced data to obtain an accurate model. In this paper, we propose a deep learning (DL)-based model for estimating the Weibull param- eters with both censoring and truncation problems. Instead of using the conventional matrices such as concordance index, we propose a novel validation framework to examine the pre- diction accuracy of different models. We examine the perfor- mance of the proposed approach on real-world power trans- former data, and the results show that our approach can im- prove prediction accuracy and is less susceptible to the trun- cation problem. Our results also suggest that deep learning techniques can help enhance traditional statistical modeling for reliability analysis.