Unsupervised Modeling of Progressive Wear in Aircraft Engines for Predictive Maintenance
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Abstract
Predicting progressive wear in aircraft engines is critical for enabling condition-based maintenance and ensuring operational reliability. A persistent challenge lies in the discrepancy between benchmark datasets and real-world engine data. Although simulated datasets offer controlled and labeled conditions for model development, they often fail to represent the full complexity, noise characteristics, and operational irregularities observed in actual flight environments. This leads to models that perform well in simulation but degrade significantly when applied in practice. To address this limitation, this work introduces a data-driven framework to simulate realistic wear-and-tear effects using high-resolution timeseries data collected over sequences of engine missions. The method infers long-term degradation patterns in an unsupervised manner, without relying on explicit wear labels, while accounting for variability introduced by mission conditions.
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