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Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition)

Authors Austin Coursey , Abel Diaz-Gonzalez , Marcos Quinones-Grueiro , Gautam Biswas

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ACM Subject Classification
  • Computing methodologies → Anomaly detection
  • Applied computing → Engineering
  • Computing methodologies → Neural networks
Keywords
  • fault detection
  • fault isolation
  • autoencoder

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Abstract

Fault detection and isolation are becoming increasingly important as modern systems become more complex. To encourage the development of new fault detection solutions that can operate with limited noisy data and an incomplete mathematical model, the DX 2025 LiU-ICE competition for diagnosis of the air path of an internal combustion engine was introduced. In this paper, we present our winning solution to this competition. Our fault detection architecture starts with a semi-supervised Transformer Autoencoder trained to reconstruct nominal data. Detected faults are then passed through a rule-based fault persistence filter that aims to suppress false positives. Once a fault is detected, we use four neural networks trained to estimate features determined from structural analysis of a partial system model. The residuals of these networks are fed to a supervised fault classification network that estimates the fault probabilities. With this architecture, we achieved an 87% detection rate with a 0% false alarm rate on the provided competition data. Additionally, our isolation architecture assigned the correct fault 73.8% probabilty on average. On unseen competition data from a new driving cycle, we achieved a 100% detection rate and assigned the correct fault 66.2% probability on average. On the other hand, the Transformer Autoencoder failed to transfer to the new driving conditions, causing many false alarms. We discuss ways future work can reduce this.

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Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, and Gautam Biswas. Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 15:1-15:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.DX.2025.15

Author Details

Austin Coursey
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA
Abel Diaz-Gonzalez
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA
Marcos Quinones-Grueiro
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA
Gautam Biswas
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA

Funding

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2444112. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Supplementary Materials

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