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Presented to:Prognostic Working Group
15 October 2014
U.S. Army Aviation and Missile Research, Development, and
Engineering Center
Presented by:
Jean P. VreulsLead Systems Engineer
[email protected] // 256-990-6195
Diagnostic / Prognostic LaboratoryU.S. Army Aviation and Missile Research,
Development, and Engineering Center
StructuralHealth Monitoring (SHM)
It’s Eat Our Lunch!
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Structural Health Monitoring (SHM) promises to do the following:
1. Reduce Unnecessary Inspections –– By monitoring the structure maintainers can move away from usage based inspection and
only perform them when damage in suspected. This removes a potential source of damage since a disassembly can often result in damage the structure (dents, scratches that break the corrosion barrier, etc.)
2. Increased asset availability – with less scheduled maintenance an asset is available for duty
3. Reduced burden on the Warfighter– An automated inspection process frees up a serviceman for other more important tasks
4. Increases safety – automated inspections reduces the risk of missing faults
5. Reduces costs – An automated SHM enables the prediction of when a component will fail. Maintainers with
this knowledge can anticipate maintenance actions and reduces the amount of spares needed thus shortening the logistics chain. Another factor is the unscheduled maintenance is by far the most costly type in the Army. Just by reducing that will allow for a large savings.
Structural Health MonitoringStructural Health Monitoring
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1. Detection
– Is there a problem?
2. Localization
– Where is the problem?
3. Classification
– How bad is the problem?
4. Prognostication
– How long before I need a repair?
Levels of SHMLevels of SHM
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REPEATABLE DESIGN PROCESS
REPEATABLE DESIGN PROCESS
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Design FrameworkDesign Framework
MODELS &CONSTRAINTS
SENSORS OPTIMIZATION SIMULATE
SIGNAL PROCESSING
METHOD
ANALYZE
AMRDEC Design Optimizes, Physics-Based Models, and Sensors for Structural Health Monitoring…
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• Methodology Differentiator: Optimization
– High sensitivity to likely damage areas (Hotspots)
– Ability to detect damage globally
– Minimum number of sensors / Minimize cost
– Reliability
– Design robustness to modeling error and manufacturing variations
OptimizationOptimization
p
DDQD
p
QDDQ
p
D
p
CCQC
p
QCCQ
p
C
p
Q T
uTuT
u
T
xTxT
xy
Repeatable Design Process
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Use
Physics
Based
Models
Optimal Sensors & Actuator Design
• Objective functions
• Algorithm
Evaluate
Design
Trade-off’s
Simulate
DesignsImplement
AMRDEC SHM Design ProcessAMRDEC SHM Design Process
Repeatable Verified Design Process…
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DECTECTIONDECTECTION
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Diagnostics and Prognostics Lab Demonstrations
Diagnostics and Prognostics Lab Demonstrations
Rotor Wing Aircraft Roof Strap and Drag Beam
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Drag BeamNo ‘Hot Spots’Known
Drag BeamNo ‘Hot Spots’Known
Random Sensor
Random Sensor
Random Sensor
Random Sensor
Optimum Sensor
Optimum Sensor
Actuator
Yellow = ActuatorRed = Optimum SensorsPurple = Random Sensors
0.5-8 kHz Excitement
• No ‘Hot Spots’• 47 lbs part• 7e-3 lbs removed
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Drag Beam Magnet DetectionDrag Beam Magnet Detection
• Optimal• Heuristic
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Roof StrapWith ‘Hot Spots’ Known
Roof StrapWith ‘Hot Spots’ Known
Optimum Sensor
Actuator
Random Sensor
Optimum Sensor
Random Sensor
Random Sensor
Random Sensor
Yellow = ActuatorRed = Optimum SensorsPurple = Random Sensors
0.5-10 kHz Excitement
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Unanticipated DamageLoosening of One Bolt Roof Strap
Unanticipated DamageLoosening of One Bolt Roof Strap
55 in-lbs 55 in-lbs45 in-lbs Finger Tight
• Optimal• Heuristic
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Damaged Roof StrapDamaged Roof Strap
0.25”
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Expected Damage LocationDemonstrated on the Roof Strap
Expected Damage LocationDemonstrated on the Roof Strap
No Damage 0.05” Cut
0.10” Cut
0.15” Cut
0.25” Cut
• Optimal• Heuristic
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Wing FittingWing Fitting
• Three specimens were tested
• Specimens taken from wing sections of aircraft that had been in-service
• Specimens were ~ 2m x 0.5m
• Skin panel
• 3 stiffeners
• U-channel fitting
• Model developed (DOF=42762)
• Expected damage locations known
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Test SpecimensTest Specimens
(a) Front side (in airstream) (b) Back side (inside wing)
Photographs showing the front side (a) and the back side of each specimen (b)
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Wing FittingSensor Location
Wing FittingSensor Location
The 5 Sensor 1 Actuator Design was chosen
8.7
27.2
15.0
1.8
2.9 3.8
16.1
29.1
1.8
20.7
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Wing Fitting ResultsWing Fitting Results
8 Test Hrs Before Failure
Stringer
Str
inge
r
Visual Detection
Optimal Design Detection
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DetectionFighter Aircraft Clevis
DetectionFighter Aircraft Clevis
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Detection – Results Detection – Results
• Detected at 16 kcycles• 0.03” Crack• 99.999% Confidence
baseline
dam
age
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LOCALIZATIONLOCALIZATION
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• Project Purpose– Does not replace current NDE/I– Guide maintainers and inspectors smartly
to the area of damage to perform NDE/I
• Paradigm– Works by identifying areas NOT having damage
Advantages– Does not need training data– Does not need high quality models
Damage LocalizationDamage Localization
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Damage & LocalizationDamage & Localization
98.4% of the area eliminated
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LocalizationRotor Wing Aircraft 409 Beam
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CORROSION CORRELATIONCORROSION
CORRELATION
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• Two unprotected 3” x 5” steel coupons• Three sensors per coupon• One piezo-electric actuator per coupon• Salt Fog applied at elevated temperature• Pictures taken three times a day
Corrosion Correlation TestCorrosion Correlation Test
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Test Results - Two SensorsTest Results - Two Sensors
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Damage Progression - 4h 06mDamage Progression - 4h 06m
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Damage Progression - 21h 39mDamage Progression - 21h 39m
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Damage Progression - 24h 45m Damage Progression - 24h 45m
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Damage Progression - 28h 46mDamage Progression - 28h 46m
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Damage Progression - 45h 25mDamage Progression - 45h 25m
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Damage Progression - 49h 45mDamage Progression - 49h 45m
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Damage Progression - 53h 15mDamage Progression - 53h 15m
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Damage Progression - 68h 55mDamage Progression - 68h 55m
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ENVIRONMENT EFFECTSCOMPENSATION
ENVIRONMENT EFFECTSCOMPENSATION
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- Sensor- Actuator- Thermocouple
L
L
W
T1
T2
S1
S2
S3
S4
A1
H
H
Temperature CompensationDesign and Experiment
Temperature CompensationDesign and Experiment
• Blind Test
• Temperature was
random between
(-60 and 150 F)
• Two Thermocouples
• Four accelerometers
• One Piezo
• Crack was cut in stages
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Uncompensated MetricUncompensated Metric
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UncompensatedDetector Performance
UncompensatedDetector Performance
Sliding Window
TPR15.7%
FPR15.3%
FNR84.3%
TNR84.7%
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Compensated MetricCompensated Metric
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CompensatedDetector Performance
CompensatedDetector Performance
Sliding Window
TPR98.2%
FPR0.0%
FNR1.8%
TNR100.0%
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COMPOSITESCOMPOSITES
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Process Works on CompositesProcess Works on Composites
Layer # Layer Mat Orientation THK (mm)
1 (interior) DBM1708 +/-45° 0.888
2 DBM1208 +/-45° 0.558
3 C520 0° 1.14
4 C520 0° 1.14
5 C520 0° 1.14
6 C520 0° 1.14
7 C520 0° 1.14
8 C520 0° 1.14
9 DBM1208 +/-45° 0.558
10 DBM1708 +/-45° 0.888
11 3/4 Mat 0° 0.38
12 (exterior) Gelcoat 0° 0.46
Layer definitions at these stations given in SNL report.
Each color represents a different layer definition.
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Crack Growth Simulation ResultsCrack Growth Simulation Results
Undamaged
Damage Case 1
Damage Case 2
Damage Case 3
Damage Case 4
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SummarySummary
• Have created a systematic design methodology – Model based– Optimizes for
• Minimum number of sensors• Maximum Sensitivity to damage• Robustness• Fault Tolerance
• Have successfully implemented damage detectors – Cracking or corrosion– Can control significance level– Environmentally compensated
• Can localize damage to guide inspectors– Reduced maintenance man-hours per inspection
• Can estimate amount of damage– Requires data