UNCLASSIFIED FileName.pptx UNCLASSIFIED UNCLASSIFIED Presented to: Prognostic Working Group 15...

UNCLASSIFIED FileName.pptx

UNCLASSIFIED

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

Jean.vreuls@us.army.mil // 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

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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

Repeatable Design Process

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Physics

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

Rotor Wing Aircraft Roof Strap and Drag Beam

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Drag BeamNo ‘Hot Spots’Known

Random 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

Optimum Sensor

Actuator

Random Sensor

Optimum 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

55 in-lbs 55 in-lbs45 in-lbs Finger Tight

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Damaged Roof StrapDamaged Roof Strap

0.25”

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Expected Damage LocationDemonstrated on the Roof Strap

No Damage 0.05” Cut

0.10” Cut

0.15” Cut

0.25” Cut

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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

The 5 Sensor 1 Actuator Design was chosen

2.9 3.8

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Wing Fitting ResultsWing Fitting Results

8 Test Hrs Before Failure

Stringer

Visual Detection

Optimal Design Detection

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DetectionFighter Aircraft Clevis

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Detection – Results Detection – Results

• Detected at 16 kcycles• 0.03” Crack• 99.999% Confidence

baseline

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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 - 24h 45m Damage Progression - 24h 45m

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ENVIRONMENT EFFECTSCOMPENSATION

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- Sensor- Actuator- Thermocouple

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

Sliding Window

TPR15.7%

FPR15.3%

FNR84.3%

TNR84.7%

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Compensated MetricCompensated Metric

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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

UNCLASSIFIED FileName.pptx UNCLASSIFIED UNCLASSIFIED Presented to: Prognostic Working Group 15...

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