1. 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

2. 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.