Health-Margin-Aware Load Redistribution from Calibrated Sensor Evidence

Condition monitoring can identify abnormal operating states, but a diagnostic posterior does not specify how much load a remaining unit can accept after a dropout. We evaluate a sensor-to-action interface that converts calibrated condition probabilities into a bounded numerical recovery request using 1449 stable cycles from the UCI hydraulic-systems dataset. A four-way split is repeated for 10 seeds. Regularization is selected on validation; temperature, the lower-margin multiplier, the conformal threshold, and the quality-gate threshold are selected on calibration; the test fold is used once for final evaluation. Mean macro-F1 is 0.985 on clean cycles, 0.672 with 10% temporal-SD noise, and 0.766 after VS1 imputation. Relative to posterior-mean requests, the calibration-selected lower margin reduces request exceedance and modeled shortfall under all three inputs. Full recovery increases only under 10% noise (+0.0615); it is essentially unchanged under clean input (−0.0054, descriptive cluster interval −0.0212 to +0.0076) and lower after VS1 imputation (−0.0219, −0.0429 to −0.0019). A fixed κ=0.5 lower margin matches or modestly exceeds the calibration-selected margin in these matched numerical episodes, so calibration selection is not claimed to provide an additional action benefit. In condition-holdout tests under noise, family-level macro-F1 is 0.289–0.354; the worst held-out level is 0.177 and the lowest numerical recovery rate is 0.033. The quality gate abstains on all noisy holdout episodes. At q=0.95, an equal-z negative-control gate reduces automatic release from 0.583 to 0.333, with corresponding changes in conditional risk and recovery. Projection enforces the configured envelope, but map, envelope, and delay mismatch can produce true-envelope violations. These results are limited to a numerical model and do not constitute physical, HIL, field, safety, or calibrated-capacity validation.