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

TL;DR

This paper introduces I-GLIDE, a novel framework for health indicator construction that improves RUL prediction accuracy and interpretability by using indicator groups and uncertainty quantification, validated on aerospace and manufacturing data.

Contribution

It adapts RaPP as a health indicator, incorporates uncertainty quantification, and proposes indicator groups for interpretable, mechanism-specific degradation modeling.

Findings

RaPP outperforms traditional reconstruction error metrics.

Uncertainty quantification enhances RUL prediction robustness.

Indicator groups improve interpretability and system-specific diagnostics.

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.