Risk-Aware Predictive Maintenance for Aero-Engines via CVaR-Guided Reinforcement Learning and LLM-Based Risk Abstraction.

Authors

Bolin Song, Yingqing Guo, Zaiyang Yu, Yao Ni, Wangyu Wu

Published in

Sensors (Basel, Switzerland). Volume 26. Issue 20. Oct 13, 2026. Epub Oct 13, 2026.

Abstract

A fundamental limitation of current predictive maintenance is the inability to transform prognostic uncertainty into actionable decisions, which often leads to either wasteful early interventions or catastrophic failures in aero-engines. To address this, we propose the Risk-Aware Predictive Maintenance (RAPM) framework consisting of four integrated components: a probabilistic stacked sparse autoencoder that extracts degradation features while providing distributional remaining useful life (RUL) predictions; a Monte Carlo Dropout module with Conditional Value at Risk (CVaR) that summarizes lower-tail predictive risk and produces a conservative RUL estimate for decision making; a locally deployed large language model (Qwen2.5-3B) that abstracts numerical uncertainty into discrete semantic risk levels, enabling interpretable reward shaping; and a Proximal Policy Optimization agent trained with curriculum learning to handle long-horizon sparse-reward maintenance optimization. On the NASA C-MAPSS FD001 benchmark, RAPM achieves a 96.0% optimal stopping rate with no late interventions or failure events compared with 90.2% for the strongest independent maintenance baseline. Under controlled substitution with LSTM-MCD, Transformer-MCD, CNN-MCD, and Deep Ensemble prognostic backbones, the same CVaR-LLM-PPO decision architecture maintains optimal stopping rates of 92.4-95.2% with no late interventions or failure events. Within the matched Rule-PPO ablation, CVaR increases the mean optimal stopping rate from 92.0% to 95.0% under unperturbed conditions, which is a gain of 3.0 percentage points. Under Gaussian sensor noise (σ=0.05), the gain reaches 9.0 percentage points, from 73.0% to 82.0%. These results demonstrate that the decision benefit of RAPM extends across independently trained prognostic models.

PMID:
42944336
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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