Transcript Fault diagnostics- bringing together DSP and Intelligent Decision

DIAGNOSTICS
Sensing
DSP and Data Fusion
Failure Feature Extraction
Diagnosis Reasoning
Inject probe test signals for refined diagnosis
Sensor
outputs
machines
Math models
x  f ( x, u ,  )
y  h ( x, u ,  )
Physics of failure
System dynamics
Physical params. 
Dig. Signal
Processing
System
IdentificationKalman filter
NN system ID
RLS, LSE
Sensor
Fusion
Vibration
Moments,
FFT
ˆ
Physical
Parameter
estimates &
Aero. coeff.
estimates
Stored Legacy Failure data
Statistics analysis
Feature
VectorsSufficient
statistics
Feature
fusion
Feature extraction
Determine inputs for diagnostic models
 (t )
Feature
vectors
Diagnostic Models
Feature patterns for faults
Decision fusion could use:
Fuzzy Logic
Expert Systems
NN classifier
Set Decision Thresholds
Manuf. variability data
Usage variability
Mission history
Minimize Pr{false alarm}
Baseline perf. requirements
Use physics of failure and failure models to select failure features to include in feature vectors  (t )
Identify
Faults/
Failures
yes
Inform
pilot
Inform
pilot
yes
More info
needed?
Serious?
PROGNOSTICS
 (t )
Fault tolerance
limits
Normal
operating
region
t
Early
mortality
t
wearout
Hazard FunctionProbability of failure
at current time
Based on legacy data and
current sensor readings
Track Feature vector trends
Study  (t ) and (t )
Fault tolerance limits found by
legacy data statistics
Estimate Remaining Useful Life with Confidence Intervals
Legacy Data Statistics gives MTBF, etc.