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DOI: 10.4230/OASIcs.DX.2026.19

Uncertainty-Aware Fault Diagnosis of Unknown Faults Using Ensemble-Based NODE Residuals (Extended Abstract)

Authors: Daniel Jung and Theodor Westny

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract

This extended abstract presents an uncertainty-aware approach to data-driven fault diagnosis using ensembles of neural ordinary differential equations (NODE). The proposed method leverages physical insights in the design of NODE models for residual generation and employs bootstrap ensembles to quantify prediction uncertainty and adapt detection thresholds. The adaptive thresholds enable robust detection and isolation of faults in scenarios with limited training data and reduces the risk of false alarms due to out-of-distribution data. The methodology is validated on real-world data from the LiU-ICE benchmark, providing insights into how training data quality and residual design impact uncertainty-aware fault diagnosis.

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Daniel Jung and Theodor Westny. Uncertainty-Aware Fault Diagnosis of Unknown Faults Using Ensemble-Based NODE Residuals (Extended Abstract). In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 19:1-19:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{jung_et_al:OASIcs.DX.2026.19,
  author =	{Jung, Daniel and Westny, Theodor},
  title =	{{Uncertainty-Aware Fault Diagnosis of Unknown Faults Using Ensemble-Based NODE Residuals}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{19:1--19:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.19},
  URN =		{urn:nbn:de:0030-drops-278334},
  doi =		{10.4230/OASIcs.DX.2026.19},
  annote =	{Keywords: Data-driven diagnosis, model-based diagnosis, structural methods, neural ordinary differential equations, ensemble models}
}

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Uncertainty-Aware Fault Diagnosis of Unknown Faults Using Ensemble-Based NODE Residuals (Extended Abstract)

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