I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
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.
692808b8243b2216fb75ce41
Lucas Thil
claimed_verified
2025-11-27T08:58:03.581Z
63e24a0cf0740bec2bfd7f9e
https://cdn-avatars.huggingface.co/v1/production/uploads/63e24a0cf0740bec2bfd7f9e/VS786oOKIF9IqIm3xnniS.jpeg
Lucas Thil
LucasThil
user
LucasThil
692808b8243b2216fb75ce42
Jesse Read
692808b8243b2216fb75ce43
Rim Kaddah
692808b8243b2216fb75ce44
Guillaume Doquet
I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
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.
A novel framework using RaPP and uncertainty quantification improves RUL prediction accuracy and interpretability in multi-sensor systems.
Reconstruction along Projected Pathways (RaPP) aleatoric uncertainty epistemic uncertainty Monte Carlo dropout probabilistic latent spaces indicator groups I-GLIDE anomaly detection prognostics