Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Jose Ignacio Aizpurua, Victoria M. Catterson, Brian G. Stewart, Stephen D. J. McArthur, Brandon Lambert, James G. Cross

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
38 Downloads (Pure)


Transformers are critical assets for the reliable operation of the power grid. Transformers may fail in service if monitoring models do not identify degraded conditions in time. Dissolved gas analysis (DGA) focuses on the examination of dissolved gasses in transformer oil to diagnose the state of a transformer. Fusion of black-box (BB) classifiers, also known as an ensemble of diagnostics models, have been used to improve the accuracy of diagnostics models across many fields. When independent classifiers diagnose the same fault, this method can increase the veracity of the diagnostics. However, if these methods give conflicting results, it is not always clear which model is most accurate due to their BB nature. In this context, the use of white-box (WB) models can help resolve conflicted samples effectively by incorporating uncertainty information and improve the classification accuracy. This paper presents an uncertainty-aware fusion method to combine BB and WB diagnostics methods. The effectiveness of the proposed approach is validated using two publicly available DGA datasets.

Original languageEnglish
Pages (from-to)621-633
Number of pages13
JournalIEEE Transactions on Systems, Man and, Cybernetics: Systems
Issue number1
Early online date3 Dec 2018
Publication statusPublished - 1 Jan 2021


  • condition monitoring
  • transformer diagnostics
  • ensembles
  • classifiers
  • uncertainty


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