UNCLASSIFIED FileName.pptx UNCLASSIFIED UNCLASSIFIED Presented to: Prognostic Working Group 15 October 2014 U.S. Army Aviation and Missile Research, Development, and Engineering Center Presented by: Jean P. Vreuls Lead Systems Engineer // Diagnostic / Prognostic Laboratory U.S. Army Aviation and Missile Research, Development, and Engineering Center Structural Health Monitoring (SHM) It’s Eat Our Lunch!

UNCLASSIFIED FileName.pptx UNCLASSIFIED 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 Monitoring

UNCLASSIFIED FileName.pptx UNCLASSIFIED 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 SHM

UNCLASSIFIED FileName.pptx UNCLASSIFIED REPEATABLE DESIGN PROCESS

UNCLASSIFIED FileName.pptx UNCLASSIFIED Design Framework MODELS & CONSTRAINTS SENSORS OPTIMIZATION SIMULATE SIGNAL PROCESSING METHOD ANALYZE AMRDEC Design Optimizes, Physics-Based Models, and Sensors for Structural Health Monitoring…

UNCLASSIFIED FileName.pptx UNCLASSIFIED 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 Optimization Repeatable Design Process

UNCLASSIFIED FileName.pptx UNCLASSIFIED AMRDEC SHM Design Process Repeatable Verified Design Process…

UNCLASSIFIED FileName.pptx UNCLASSIFIED DECTECTION

UNCLASSIFIED FileName.pptx UNCLASSIFIED Diagnostics and Prognostics Lab Demonstrations Rotor Wing Aircraft Roof Strap and Drag Beam

UNCLASSIFIED FileName.pptx UNCLASSIFIED Drag Beam No ‘Hot Spots’Known Random Sensor Optimum Sensor Actuator Yellow = Actuator Red = Optimum Sensors Purple = Random Sensors kHz Excitement No ‘Hot Spots’ 47 lbs part 7e-3 lbs removed

UNCLASSIFIED FileName.pptx UNCLASSIFIED Drag Beam Magnet Detection Optimal Heuristic

UNCLASSIFIED FileName.pptx UNCLASSIFIED Roof Strap With ‘Hot Spots’ Known Optimum Sensor Actuator Random Sensor Optimum Sensor Random Sensor Yellow = Actuator Red = Optimum Sensors Purple = Random Sensors kHz Excitement

UNCLASSIFIED FileName.pptx UNCLASSIFIED Unanticipated Damage Loosening of One Bolt Roof Strap 55 in-lbs 45 in-lbsFinger Tight Optimal Heuristic

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damaged Roof Strap 0.25”

UNCLASSIFIED FileName.pptx UNCLASSIFIED Expected Damage Location Demonstrated on the Roof Strap No Damage0.05” Cut 0.10” Cut 0.15” Cut 0.25” Cut Optimal Heuristic

UNCLASSIFIED FileName.pptx UNCLASSIFIED Wing 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

UNCLASSIFIED FileName.pptx UNCLASSIFIED Test 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)

UNCLASSIFIED FileName.pptx UNCLASSIFIED Wing Fitting Sensor Location The 5 Sensor 1 Actuator Design was chosen

UNCLASSIFIED FileName.pptx UNCLASSIFIED Wing Fitting Results 8 Test Hrs Before Failure Stringer Visual Detection Optimal Design Detection

UNCLASSIFIED FileName.pptx UNCLASSIFIED Detection Fighter Aircraft Clevis

UNCLASSIFIED FileName.pptx UNCLASSIFIED Detection – Results Detected at 16 kcycles 0.03” Crack % Confidence baseline damage

UNCLASSIFIED FileName.pptx UNCLASSIFIED LOCALIZATION

UNCLASSIFIED FileName.pptx UNCLASSIFIED 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 Localization

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage & Localization 98.4% of the area eliminated

UNCLASSIFIED FileName.pptx UNCLASSIFIED Localization Rotor Wing Aircraft 409 Beam

UNCLASSIFIED FileName.pptx UNCLASSIFIED CORROSION CORRELATION

UNCLASSIFIED FileName.pptx UNCLASSIFIED 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 Test

UNCLASSIFIED FileName.pptx UNCLASSIFIED Test Results - Two Sensors

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 4h 06m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 21h 39m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 24h 45m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 28h 46m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 45h 25m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 49h 45m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 53h 15m

UNCLASSIFIED FileName.pptx UNCLASSIFIED Damage Progression - 68h 55m

UNCLASSIFIED FileName.pptx UNCLASSIFIED ENVIRONMENT EFFECTS COMPENSATION

UNCLASSIFIED FileName.pptx UNCLASSIFIED - Sensor - Actuator - Thermocouple L L W T1 T2 S1 S2 S3 S4 A1A1 H H Temperature Compensation Design and Experiment Blind Test Temperature was random between (-60 and 150 F) Two Thermocouples Four accelerometers One Piezo Crack was cut in stages

UNCLASSIFIED FileName.pptx UNCLASSIFIED Uncompensated Metric

UNCLASSIFIED FileName.pptx UNCLASSIFIED Uncompensated Detector Performance Sliding Window TPR 15.7% FPR 15.3% FNR 84.3% TNR 84.7%

UNCLASSIFIED FileName.pptx UNCLASSIFIED Compensated Metric

UNCLASSIFIED FileName.pptx UNCLASSIFIED Compensated Detector Performance Sliding Window TPR 98.2% FPR 0.0% FNR 1.8% TNR 100.0%

UNCLASSIFIED FileName.pptx UNCLASSIFIED COMPOSITES

UNCLASSIFIED FileName.pptx UNCLASSIFIED Process Works on Composites Layer #Layer MatOrientationTHK (mm) 1 (interior)DBM1708+/-45° DBM1208+/-45° C5200°1.14 4C5200°1.14 5C5200°1.14 6C5200°1.14 7C5200°1.14 8C5200°1.14 9DBM1208+/-45° DBM1708+/-45° /4 Mat0° (exterior)Gelcoat0°0.46 Layer definitions at these stations given in SNL report. Each color represents a different layer definition.

UNCLASSIFIED FileName.pptx UNCLASSIFIED Crack Growth Simulation Results Undamaged Damage Case 1 Damage Case 2 Damage Case 3 Damage Case 4

UNCLASSIFIED FileName.pptx UNCLASSIFIED Summary 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