融合时序特征的高斯过程预测航空发动机剩余寿命区间，兼顾精度与可解释性。
Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
- 用改进高斯过程结合深度自适应学习捕捉制造时序动态特征
- 通过历史数据学习生成置信区间，提升不确定性建模能力
- 评估特征重要性，实现故障预测过程透明可解释
剩余使用寿命（RUL）估计在智能制造系统和工业4.0技术中至关重要。尽管近年来预测性能有所提升，但多数模型仍面临可解释性差和不确定性建模不足的问题。本文提出一种改进的高斯过程回归（GPR）模型，用于航空发动机健康状态预测中的剩余使用寿命区间预测。该模型通过深度自适应学习增强的AI流程，有效捕捉现代制造系统中的复杂时序模式与动态行为，并基于历史数据学习生成置信区间，实现更结构化的不确定性建模。同时，模型评估各特征的重要性，提升决策透明度，对优化制造流程具有重要意义。该方法显著提高了预测准确性，并提供了可解释的不确定性分析，有助于实现稳健的工艺开发与管理。
原文摘要 · Abstract (English)
The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many models still face interpretability and compelling uncertainty modeling challenges. This paper introduces a modified Gaussian Process Regression (GPR) model for RUL interval prediction, tailored for the complexities of manufacturing process development. The modified GPR predicts confidence intervals by learning from historical data and addresses uncertainty modeling in a more structured way. The approach effectively captures intricate time-series patterns and dynamic behaviors inherent in modern manufacturing systems by coupling GPR with deep adaptive learning-enhanced AI process models. Moreover, the model evaluates feature significance to ensure more transparent decision-making, which is crucial for optimizing manufacturing processes. This comprehensive approach supports more accurate RUL predictions and provides transparent, interpretable insights into uncertainty, contributing to robust process development and management.
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