In-Flight Acoustic Emission Fatigue Crack MonitoringIn-Flight Acoustic Emission Fatigue Crack Monitoring Eric v. K. Hill, Robert J. Demeski, Christopher L. Rovik and Samuel G. Vaughn IIIEric v. K. Hill, Robert J. Demeski, Christopher L. Rovik and Samuel G. Vaughn III
APPROACH/TECHNICAL CHALLENGES
• Monitor fatigue cracking in notched 7075-T6 aluminum channel beam in laboratory
• Train SOM neural network to accurately classify fatigue crack growth AE signals from laboratory
• In-flight AE fatigue crack monitoring: Piper Cadet engine cowling and Cessna Crusader T-tail
ACCOMPLISHMENTS/RESULTS• Detected fatigue crack growth: Piper Cadet engine Piper Cadet engine
cowling during ground operations and Cessna cowling during ground operations and Cessna Crusader T-tail during roll/Dutch roll maneuversCrusader T-tail during roll/Dutch roll maneuvers
OBJECTIVES
•Use self-organizing map (SOM) neural network to classify acoustic emission (AE) failure mechanism data into fatigue cracking, plastic deformation (ahead of crack tip) and rubbing/fretting noises
•Goal:Goal: Demonstrate in-flight AE fatigue crack monitoring – minimize maintenance costs and minimize maintenance costs and extend service lives of aging aircraftextend service lives of aging aircraftNotched 7075-T6 Aluminum Channel Notched 7075-T6 Aluminum Channel
Beam Fatigue Specimen with Riveted Beam Fatigue Specimen with Riveted and Bolted Attachmentsand Bolted Attachments
AE SourceAE Source Duration [µs]Duration [µs] Amplitude [dB]Amplitude [dB]
Fatigue CrackingFatigue Cracking 0-6,000 0-6,000 65-10065-100
Plastic DeformationPlastic Deformation 0-6,0000-6,000 30-6530-65
Rubbing/FrettingRubbing/Fretting 6,000-32,0006,000-32,000 30-7030-70
Fatigue Specimen with AE Transducers in MTS MachineFatigue Specimen with AE Transducers in MTS Machine
AE Source Location PlotAE Source Location Plot
Fatigue Cracking - s0 Lamb Wave
Fatigue Cracking - a0 Lamb Wave Rubbing/Fretting
SOMSOMClassifierClassifier
Laboratory Fatigue Crack Growth MonitoringLaboratory Fatigue Crack Growth Monitoring
AE Signal ParametersAE Signal Parameters
Piper PA-28 Cadet Engine CowlingPiper PA-28 Cadet Engine Cowling
ExpectedExpected Fatigue Crack Fatigue Crack
Data Between AE Data Between AE Transducers 1 & 2Transducers 1 & 2
Unexpected Unexpected Fatigue Crack Fatigue Crack
Data Between AE Data Between AE Transducers 3 & 4Transducers 3 & 4
25-Sept-97(Chan. 1&2)Manuever Total hits Crk hits % Rub hits % Plst. hits %
Taxi 16373 771 4.7% 502 3.1% 15099 92.2%Take-Off 16734 496 3.0% 648 3.9% 15229 91.0%
Climb Out 234 0 0.0% 233 99.6% 1 0.4%Steady Level Flight 132 0 0.0% 132 100.0% 0 0.0%Final/Touch and Go 287 1 0.3% 285 99.3% 1 0.3%
10-Oct-97(Chan. 1&2)Manuever Total hits Crk hits % Rub hits % Plst. hits %
Taxi 16374 1674 10.2% 104 0.6% 14594 89.1%Take-Off 12336 753 6.1% 278 2.3% 11304 91.6%
Climb Out 1086 0 0.0% 1085 99.9% 1 0.1%Steady Level Flight 2174 189 8.7% 1830 84.2% 156 7.2%
Final/Landing 16373 3004 18.3% 1334 8.1% 12030 73.5%
Engine CowlingEngine CowlingFatigue CrackingFatigue Cracking
Predominant DuringPredominant DuringGround OperationsGround Operations
Self-Organizing Map (SOM) Neural Network ResultsSelf-Organizing Map (SOM) Neural Network Results
Turbulent Propeller Turbulent Propeller Wash/EngineWash/Engine
VibrationsVibrations
Cessna T-303 Crusader T-TailCessna T-303 Crusader T-Tail
Cessna CrusaderCessna Crusader
Redundant Redundant Notched Fatigue Notched Fatigue
Structure Mounted Structure Mounted in Vertical Tailin Vertical Tail
Maneuver Crack Events Crack % PD Events PD % MN Events MN %Taxi 0 0.0% 963 93.0% 73 7.0%
Takeoff 69 14.4% 250 52.3% 159 33.3%Flight 277 23.3% 515 43.3% 398 33.4%Flight 1 0.1% 782 66.1% 400 33.8%Flight 7 3.5% 104 52.5% 87 43.9%
Dutch Roll 10 3.9% 113 44.5% 131 51.6%Roll 40 5.1% 383 48.7% 364 46.3%Roll 212 71.9% 52 17.6% 31 10.5%
Dutch Roll 204 73.9% 42 15.2% 30 10.9%Flight 472 64.1% 160 21.7% 104 14.1%
Landing 11 2.3% 331 69.0% 138 28.8%Taxi 0 0.0% 895 93.7% 60 6.3%
Fatigue Cracking in Fatigue Cracking in T-Tail RedundantT-Tail RedundantNotched StructureNotched Structure
Predominant duringPredominant duringIn-Flight OperationsIn-Flight Operations
Self-Organizing Map (SOM) Neural Network ResultsSelf-Organizing Map (SOM) Neural Network Results
Roll and Dutch RollRoll and Dutch RollManeuversManeuvers