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

• URL: http://arxiv.org/abs/2511.21208v1
• Date: Wed, 26 Nov 2025 09:39:35 GMT
• Title: I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
• Authors: Lucas Thil, Jesse Read, Rim Kaddah, Guillaume Doquet,
• Abstract summary: This paper introduces a novel framework for health indicators (HIs) construction, advancing three key contributions.<n>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.<n>We also propose indicator groups, a paradigm that isolates sensor subsets to model system-specific degradations, giving rise to our novel method, I-GLIDE.
• Score: 1.034052616244602
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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