title: Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition)
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 
journal: Leibniz-Zentrum für Informatik (Schloss Dagstuhl)