A framework learns fault-indexed Perron-Frobenius operators from trajectory data to provide certifiable 2-Wasserstein bounds for detecting actuator faults and enabling recovery via density propagation in nonlinear control-affine systems.
Data- driven probabilistic fault detection and identification vi a density flow matching
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Perron-Frobenius Contractive Operator Matching for Data-Driven Reachable Fault Identification and Recovery
A framework learns fault-indexed Perron-Frobenius operators from trajectory data to provide certifiable 2-Wasserstein bounds for detecting actuator faults and enabling recovery via density propagation in nonlinear control-affine systems.