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.

Accurate estimation of the remaining useful life (RUL) of industrial systems is a critical component of predictive maintenance strategies. This work presents a data-driven method for RUL prediction that also quantifies uncertainty, drawing inspiration from model-based particle filtering techniques. Instead of simulating system state transitions, we model degradation as a stochastic process governed by performance metrics and use a Bayesian particle filtering framework to infer its underlying parameters. Our approach bypasses traditional state-space modeling by directly estimating the end-of-life distribution from observed performance data. Key characteristics of the filter, such as propagation noise and observation correction strength, are adapted over time based on current observations and past predictive performance, enabling better capture of future uncertainty. We evaluate the proposed method using an unmanned aerial vehicle simulation dataset developed for system-level prognostics research, which includes high-fidelity degradation signals and ground-truth system performance metrics for validating predictive accuracy.