Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation
arXiv:2605.30585v1 Announce Type: new Abstract: Effective prognostics and health management of modern engines relies on accurate turbine gas temperature predictions and robust uncertainty quantification to ensure reliability and safety. This paper investigates five major approaches for constructing prediction intervals -- namely the Delta method, Bayesian Monte Carlo Dropout, Bootstrap method, Lower-Upper Bound Estimation, and Mean-Variance Estimation -- as a means of capturing the uncertainty in neural network predictions of turbine gas temperature. Each approach is implemented within a unifi
The increasing complexity and adoption of AI in critical engineering applications necessitate robust methods for understanding and quantifying prediction uncertainty, driving research in this area.
Accurate prediction and comprehensive uncertainty quantification in turbine health management are crucial for enhancing operational safety, optimizing maintenance schedules, and extending the lifespan of vital machinery.
This research contributes to refining the methodologies by which AI-driven predictive maintenance systems can provide more reliable and transparent estimates of equipment degradation, enabling better decision-making.
- · Aerospace Industry
- · Predictive Maintenance Software Developers
- · Critical Infrastructure Operators
- · Companies with unreliable predictive maintenance systems
- · Organizations with high operational safety risks
Improved reliability and safety in complex machinery through better AI-driven prognostics.
Reduced operational costs and downtime due to optimized maintenance and fewer unexpected failures.
Increased adoption of AI and machine learning in other high-stakes engineering domains due to enhanced trust in uncertainty quantification.
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