I-GLIDE：基于输入组的潜在健康指标退化估计算法

摘要

精确预测剩余使用寿命（RUL）的关键在于健康指标（HI）的质量，然而现有方法往往难以解析多传感器系统中的复杂退化机制，也无法量化健康指标可靠性的不确定性。本文提出了一种创新的健康指标构建框架，具有三大核心贡献：首先，我们首次将投影路径重构（RaPP）方法改造为适用于RUL预测的健康指标，证明其性能优于传统重构误差度量；其次，通过蒙特卡洛丢弃法和概率潜空间实现认知不确定性与偶然不确定性的量化，显著增强了RaPP衍生健康指标的RUL预测鲁棒性；第三也是最重要的，我们提出指标组新范式——通过分离传感器子集来建模系统特定退化机制，由此诞生了创新方法I-GLIDE，可实现可解释的机制特异性诊断。在航空航天与制造系统数据上的测试表明，相较于最先进的健康指标方法，我们的方案在预测精度与泛化能力上均取得显著提升，同时为系统失效路径提供了可操作的洞见。这项研究弥合了异常检测与预后预测之间的鸿沟，为复杂系统的不确定性感知退化建模提供了理论框架。

English

Accurate remaining useful life (RUL) prediction hinges on the quality of health indicators (HIs), yet existing methods often fail to disentangle complex degradation mechanisms in multi-sensor systems or quantify uncertainty in HI reliability. This paper introduces a novel framework for HI construction, advancing three key contributions. First, we adapt Reconstruction along Projected Pathways (RaPP) as a health indicator (HI) for RUL prediction for the first time, showing that it outperforms traditional reconstruction error metrics. Second, we show that augmenting RaPP-derived HIs with aleatoric and epistemic uncertainty quantification (UQ) via Monte Carlo dropout and probabilistic latent spaces- significantly improves RUL-prediction robustness. Third, and most critically, we propose indicator groups, a paradigm that isolates sensor subsets to model system-specific degradations, giving rise to our novel method, I-GLIDE which enables interpretable, mechanism-specific diagnostics. Evaluated on data sourced from aerospace and manufacturing systems, our approach achieves marked improvements in accuracy and generalizability compared to state-of-the-art HI methods while providing actionable insights into system failure pathways. This work bridges the gap between anomaly detection and prognostics, offering a principled framework for uncertainty-aware degradation modeling in complex systems.

I-GLIDE：基于输入组的潜在健康指标退化估计算法

I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation

摘要

Support