A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation
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 remains fragmented, with comparisons limited by the use of unrealistic datasets and insufficient exploration of the exploitation of temporal information. This work investigates how to recover component-level health indicators from operational sensor data under realistic degradation and maintenance patterns. To support this study, we introduce a new dataset that incorporates industry-oriented complexities such as maintenance events and usage changes. Using this dataset, we establish an initial benchmark that compa...
| Authors | Milad Leyli-Abadi, Lucas Thil, Sebastien Razakarivony et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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