Based on real degradation trajectories from the NASA Prognostics Center of Excellence (PCoE) Battery Dataset, this research program evaluates predictive feasibility, inferability, transfer stability, collapse dynamics, recovery behavior, and early-warning monitoring under real physical degradation ground truth.
This research program investigates whether predictive feasibility can be evaluated directly against true physical degradation behavior rather than only synthetic or proxy-based signals.
The program uses real lithium-ion battery degradation trajectories, state-of-health progression, run-to-failure behavior, and cross-cell variability from the NASA PCoE battery dataset.
The objective is to determine whether progression-sensitive inferability, transfer stability, collapse dynamics, and recovery behavior can be observed under real physical ground truth.
Tests whether progression-sensitive information remains available under true battery degradation ground truth.
Evaluates whether inferability structure correlates with real predictive stability.
Measures whether predictive structure survives transfer between different battery cells.
Tests robustness across different battery aging regimes and operating groups.
Links inferability collapse to measurable physical dynamics such as variability and degradation behavior.
Investigates whether voltage, current, and temperature stop moving synchronously during mapping instability.
Tests whether synchronization-risk indicators can warn before visible predictive degradation.
Models inferability degradation as staged operating states rather than isolated warning events.
Estimates transition probabilities, collapse probabilities, and future inferability state behavior.
Distinguishes stable recovery from temporary rebound after predictive collapse.
The program demonstrates that predictive feasibility is not determined solely by model choice. Observable-state stability, transfer robustness, synchronization behavior, physical degradation dynamics, and recovery trajectories all influence whether prediction remains operationally reliable.
For industrial systems, this supports a practical conclusion: predictive modeling should not begin only with model selection. It should begin by evaluating whether the signal preserves stable and reproducible predictive structure under real operating and degradation conditions.
Many predictive maintenance projects fail because prediction is assumed before predictive feasibility has been structurally evaluated.
The NASA Ground-Truth Validation Program tests whether PFA-style inferability and deployment-stability logic can be grounded in real physical degradation data.
This makes the NASA validation program one of the strongest technical foundations for the Predictive Feasibility Assessment framework.