Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
arXiv:2605.31241v1 Announce Type: new Abstract: This study presents a novel hybrid prognostic framework for uncertainty-aware Remaining Useful Life (RUL) estimation in turbofan engines using the NASA C-MAPSS dataset. The framework employs a state-aware strategy that bifurcates the engines operational lifespan into "healthy" and "degraded" regimes. An LSTM-based autoencoder, trained strictly on nominal data (RUL > 150 cycles), monitors reconstruction error to act as a robust state classifier. For the healthy regime, a Conditional Weibull Survival Analysis is used for Mean Residual Life estimati
The increasing complexity and reliance on advanced machinery, particularly in critical sectors like aerospace, necessitate more accurate and real-time predictive maintenance solutions. Advances in AI and sensor technology are making these hybrid prognostic frameworks feasible.
Accurate Remaining Useful Life (RUL) prediction is critical for optimizing maintenance schedules, reducing operational costs, enhancing safety, and improving the efficiency of complex mechanical systems, with clear implications for industries like aerospace and manufacturing.
The ability to bifurcate system health into 'healthy' and 'degraded' states and apply tailored prediction models significantly improves the accuracy and reliability of RUL estimation, moving beyond generic models to more nuanced prognostic frameworks.
- · Aerospace manufacturers
- · Airlines
- · Industrial IoT providers
- · Predictive maintenance software companies
- · Traditional preventative maintenance contractors
- · Inflexible maintenance planning systems
More efficient and safer operation of turbofan engines and similar complex machinery through advanced predictive maintenance.
Reduced unplanned downtime and maintenance costs across various industries adopting similar prognostic techniques.
The development of highly autonomous systems that can self-diagnose and potentially self-repair or schedule their own maintenance without human intervention.
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