This is like a very smart mechanic for jet engines that continuously listens to many different sensors and, using patterns learned from past engines, estimates how much life is left before something needs repair or replacement.
Predicts the remaining useful life of aeroengines more accurately from complex sensor data, enabling condition-based maintenance instead of fixed schedules, thereby reducing unplanned failures, downtime, and maintenance cost while improving safety.
Proprietary aeroengine operating and failure-history data combined with a specialized graph neural network architecture tailored to heterogeneous, time-varying sensor relationships creates a performance edge that is difficult for generic predictive models to match.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Training and inference cost of graph neural networks on long multivariate time series for large engine fleets, plus data volume and labeling requirements for diverse operating conditions.
Early Adopters
Uses a heterogeneous, dynamic-aware graph neural network architecture to explicitly model changing relationships among different engine components and sensors over time, improving RUL prediction versus traditional sequence models (e.g., plain LSTMs) or hand-crafted health indices.