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

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cs.LG2026

A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation

Milad Leyli-Abadi, Lucas Thil, Sebastien Razakarivony +2

Estimating the health state of turbofan engines is a challenging ill-posed inverse problem, hindered by sparse sensing and complex nonlinear thermodynamics. Research in this area r…

cs.LG2025★ 1 cited

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

Lucas Thil, Jesse Read, Rim Kaddah +1

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 m…