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

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Jiaxiang Cheng Sungin Cho Yap Peng Tan Guoqiang Hu

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.

Abstract 201 | PDF Downloads 246

##plugins.themes.bootstrap3.article.details##

Keywords

Failure analysis, Deep learning, Weibull distribution, Model validation, Model estimation

References
Bennis, A., Mouysset, S., & Serrurier, M. (2020). Estimation of conditional mixture weibull distribution with right censored data using neural network for time-to-event analysis. In (Vol. 12084, p. 687-698).

Bennis, A., Mouysset, S., & Serrurier, M. (2021). DPWTE: A deep learning approach to survival analysis using a parsimonious mixture of weibull distributions. In Arti- ficial neural networks and machine learning – ICANN 2021 (p. 185-196). Springer International Publishing.

Chmura, L., Morshuis, P. H. F., Gulski, E., Smit, J. J., & Janssen, A. (2011). Statistical analysis of subcom- ponent failures in power transformers. In 2011 IEEE electrical insulation conference (EIC). Fletcher, R. (2013). Practical methods of optimization. John Wiley & Sons.

Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Pre- diction of remaining life of power transformers based on left truncated and right censored lifetime data. The Annals of Applied Statistics, 3(2), 857–879.

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treat- ment recommender system using a cox proportional hazards deep neural network. BMC medical research methodology, 18(1), 1–12.

Nagpal, C., Li, X., & Dubrawski, A. (2021). Deep survival machines: Fully parametric survival regression and representation learning for censored data with compet- ing risks. IEEE Journal of Biomedical and Health In- formatics, 25(8), 3163–3175.

Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics.
Section
Regular Session Papers