DIAGNOSTICS Sensing DSP and Data Fusion Failure Feature Extraction Diagnosis Reasoning Inject probe test signals for refined diagnosis Inform pilot Vibration Moments, FFT Stored Legacy Failure data Statistics analysis Identify Faults/ Failures yes Inform pilot Sensor outputs Dig. Signal Processing Feature Vectors- Sufficient statistics Diagnostic Models Feature patterns for faults Decision fusion could use: Fuzzy Logic Expert Systems NN classifier More info needed? yes machines Serious? System Identification- Kalman filter NN system ID RLS, LSE Math models Physical Parameter estimates & Aero. coeff. estimates Feature vectors Set Decision Thresholds Manuf. variability data Usage variability Mission history Minimize Pr{false alarm} Baseline perf. requirements Feature fusion Sensor Fusion Physics of failure System dynamics Physical params. Feature extraction Determine inputs for diagnostic models Use physics of failure and failure models to select failure features to include in feature vectors

Track Feature vector trends Study and Hazard Function- PROGNOSTICS Fault tolerance limits Normal operating region t t Early mortality wearout Track Feature vector trends Study and Hazard Function- Probability of failure at current time Fault tolerance limits found by legacy data statistics Based on legacy data and current sensor readings Estimate Remaining Useful Life with Confidence Intervals Legacy Data Statistics gives MTBF, etc.