TY - GEN
T1 - I-GLIDE
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025
AU - Thil, Lucas
AU - Read, Jesse
AU - Kaddah, Rim
AU - Doquet, Guillaume
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/1/1
Y1 - 2026/1/1
N2 - 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.
AB - 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.
KW - Degradation Modeling
KW - Health Indicator
KW - Latent Space
UR - https://www.scopus.com/pages/publications/105020008863
U2 - 10.1007/978-3-032-06106-5_23
DO - 10.1007/978-3-032-06106-5_23
M3 - Conference contribution
AN - SCOPUS:105020008863
SN - 9783032061058
T3 - Lecture Notes in Computer Science
SP - 395
EP - 411
BT - Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Proceedings
A2 - Ribeiro, Rita P.
A2 - Soares, Carlos
A2 - Gama, João
A2 - Pfahringer, Bernhard
A2 - Japkowicz, Nathalie
A2 - Larrañaga, Pedro
A2 - Jorge, Alípio M.
A2 - Abreu, Pedro H.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 15 September 2025 through 19 September 2025
ER -