2026-07-21T23:48:03Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:24804 2026-02-09T07:47:35Z ddc:004 open_access

Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition) Coursey, Austin Diaz-Gonzalez, Abel Quinones-Grueiro, Marcos Biswas, Gautam fault detection fault isolation autoencoder 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Austin Coursey and Abel Diaz-Gonzalez and Marcos Quinones-Grueiro and Gautam Biswas 2025 Is Part Of OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2025.15 urn:nbn:de:0030-drops-248043 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.15 eng https://creativecommons.org/licenses/by/4.0/legalcode