GEAR HEALTH ALGORITHM SOLUTION FOR DRIVE SYSTEM DESIGN AND OPERATIONS

1. GEAR HEALTH ALGORITHM SOLUTION
FOR DRIVE SYSTEM DESIGN AND
OPERATIONS
Raja V. Pulikollu
Sentient Science
AHS Conference, Forum 72
6/9/2016

2. Acknowledgements
6/9/2016
Prognostic Model for Rotorcraft Drive System
• U.S. Army Aviation Applied Technology Directorate
(AATD)
– Bruce Thompson, Treven Baker, Clay Ames, Matt
Spies
• The Boeing Company
– Tony Shen, Doug Knapp, Steve Slaughter, Alice
Murphy

3. Applying Material Science & Computational Testing
to Determine Component & System Failure Rates
Introducing Sentient Science
Prognostic Model for Rotorcraft Drive System
2001-2016 2010 2014 April 2016
6/9/2016

4. Army Vision for Prognostics
6/9/2016
Prognostic Model for Rotorcraft Drive System
2009 white paper from Army Research Lab,
showing value of integrating physics-based models
with HUMS data
US Army Research Lab
Proposed Model
“This paper presents physics-based models as
a key component of prognostic and diagnostic
algorithms of health monitoring systems.”

5. Overall Objectives
6/9/2016
Prognostic Model for Rotorcraft Drive System
• Develop and integrate new prognostic technologies to ensure safe, reliable,
and efficient operation and maintenance of rotorcraft drive systems
• Assist current Aviation Science and Technology (S&T) Strategic Plan
(ASSP) initiatives to transition from CBM to enhanced CBM with extended
Maintenance Free Operating Periods (MFOP) and finally, to the Zero
Maintenance (ZM) vision
• Develop, verify and validate prognostic modeling capabilities for a drive
system planetary gear

6. Drives System Rating and Life Calculation
Conventional Approach
6/9/2016
Prognostic Model for Rotorcraft Drive System
- AGMA provides notional deterministic,
conservative fatigue life curve based on
empirical factors
- Conventional approach doesn’t
account for the gears material
properties and processing methods
- Due to lack of test data, a 10:1
reduction in allowable cycles was
recommended (conservative approach)

7. Drives System Rating and Life Calculation
Materials-Based Prognostics Approach
1. Determine the Critical Components
Driving Gearbox Life
2. Characterize & Compare
Microstructure
Material Models
3. Apply Computational Tribology Simulation
5. Predict Failure Mode Outcomes in
Repeated Steps
4. Simulate Stress in Microstructure to Predict
Crack Initiation & Propagation
6. Report on Damage Mode and Fatigue
Life Distribution
Prognostic Model for Rotorcraft Drive System
6/9/2016

8. Planetary Gear System
Multi-Body Dynamic Model For Load Analysis
6/9/2016
Prognostic Model for Rotorcraft Drive System
Created mesh
stiffness using
Abaqus model
Created bearing
stiffness matrix using
bearing geometry
details
Created system model using assembly dimension
and relative position from step file

9. 6/9/2016
Prognostic Model for Rotorcraft Drive System
Planetary Gear System
Multi-Body Dynamic Model For Load Analysis
• During rotorcraft operation drive system gears may be subjected to over load conditions (i.e., greater
than MCP) that may cause considerable damage to the gearbox
• Developed computational models of different components in planetary system
• Analyzed stresses translated from system/component loads
• Determined high stress regions of sun gear, component of interest
in MPa
Sun
Gear

10. Stochastic Microstructure Model – Sun Gear
6/9/2016
Prognostic Model for Rotorcraft Drive System
• Pyrowear 53 (AMS 6308) and AISI 9310 (AMS 6265) gear
alloys are typically used in drive systems.
• Characterized Material Microstructure of AMS 6265 and AMS
6308 materials
• The microstructure model inputs are developed by
considering the metallurgical elements, the manufacturing
processes, residual stress profile, bulk material properties
and the surface finish
• Uniform tempered martensite with no noticeable inclusions
that could be detrimental to gear performance by causing
early crack initiation and lower fatigue life.
• Developed prognostic fatigue life model based on planetary
gear material microstructure response to the applied loading
conditions

11. Mixed-EHL Model For Sun Gear
Contact Stress Analysis
6/9/2016
Prognostic Model for Rotorcraft Drive System
• The influence of microasperity contact was into
account when modeling surface fatigue and predicting
the probabilistic life
• Mixed-EHL solver utilizes real (simulated) surface
roughness profiles in an explicit-deterministic
calculation of surface tractions
– Outcome: Determine the performance of a given
surface finish during the generation, sustainment,
and/or failure of an EHL film at the contact zone.
Sample RMS Sq (in)
Average
Sa (in)
Skewness
Ssk
Kurtosis
Sku
AMS 6308 14 11 -0.34 16
AMS 6265 14 11 -1.1 34
Mobil AGL properties, input to mixed-EHL model:
Operating Temperature 200 F
Absolute Viscosity 0.009648 kg/(m s)
Pressure Viscosity Coefficient 9.5e-9 /Pascal

12. 6/9/2016
Prognostic Model for Rotorcraft Drive System
Fatigue Life Modeling
Sun Gear Bending Fatigue at Overload Conditions
N/A N/A N/A
1.1 6.25E+09 92500
2.1 1.46E+05 31592
197Ksi
Run-out
141Ksi
Max Fillet
Stress, Ksi
L1, Rev L1, hours L10, Rev L10, Hours
70 Run-out Run-out Run-out Run-out
141 1.00E+08 1276.16131 8.00E+08 10209.2905
197 4.94E+04 0.63042369 8.73E+04 1.11408882
StatisticalPercentageof
GearsFailed
Gear Revolutions Gear Revolutions
3-parameter Weibull StatsFatigue Life Predictions
• Prognostic model was used to simulate the effects of the overload conditions on sun gear
• Predicted no failures at 70.7 Ksi (100%MCP), and risk of tooth loss at overloads
• Bending fatigue life scatter reduced with increase in overload

13. 6/9/2016
Prognostic Model for Rotorcraft Drive System
Fatigue Life Modeling
Sun Gear Bending Fatigue at Overload Conditions
• AMS 6308 material-based prognostic model results at 9 overload cases was used to
generate stress (S) – revolutions (N) curve and identify endurance limit.
• Nominal fatigue endurance limit for bending is 140 Ksi (stress ratio, R= -0.01) based on
model calculations. These fatigue endurance limits and overload fatigue life predictions
correlated well with physical test data validating the modeling approach

14. Fatigue Life Modeling
Sun Gear Contact Fatigue at Overload Conditions
6/9/2016
Prognostic Model for Rotorcraft Drive System
• Max contact Pressure at Overload: 253.7 Ksi
• Pitting is the dominant damage mode. Crack initiation location: 10 and 120µm into the depth
• DigitalClone calculates micro-stress response of the material to the applied loading/traction
Contact Surface
Contact Surface
Crack initiation depths: 10
um and 120.82um
Pit Width: 76.46um
Pit Depth: 201.16um
Life (rev): 5.41E+07
Towards TipTowards Root
Surface material loss
AMS 6308 subsurface microstructure

15. 6/9/2016
Prognostic Model for Rotorcraft Drive System
Fatigue Life Modeling
Sun Gear Contact Fatigue at Overload Conditions
AMS 6265
AMS 6308
L10 Life L50 Life L90 Life
AMS 6265 Gear Rev 2.59E+06 2.16E+07 1.77E+08
AMS 6308 Gear Rev 4.7E+06 5.66E+07 8.21E+08
AMS 6308/AMS
6265 Gear Life
1.8 2.6 4.6
Maximum contact pressure: 253.7 Ksi
• Prognostic model was also used to compare contact fatigue performance of AMS 6265
and AMS 6308 gears
• Ground finish AMS 6308 gear rolling contact fatigue (RCF) life is higher compared to
AMS 6265 gear due to overall superior heat treat process and microstructure

16. Prognostic Model Integration and
Demonstration For Autonomous Monitoring
6/9/2016
Prognostic Model for Rotorcraft Drive System
Predict
Monitor
Assess
Adjust
PREDICT: Use first principles analysis to create
physics-based models of dynamic systems
DIGITALCLONE
MONITOR: Collect current state data and
evaluate status of health indicators
HUMS
ASSESS: Use load estimates and damage
propagation models to determine usage impact
REGIME RECOGNITION
ADJUST: Apply damage/life penalties to physics-
based models, as required to update predictions
AUTOMATED MODEL UPDATE
• The goal is to integrate prognostic model
fatigue life results with industry existing on
and off-board processors, HUMS for safe
operation and reduce O&M costs

17. 6/9/2016
Prognostic Model for Rotorcraft Drive System
Prognostic Model Integration and
Demonstration For Autonomous Monitoring
Level Event Actions
Pilot
- Prognostics provide system health state, and
mean time to observable damage (MTOD) life in
hours.
- CBM+ to confirm predictions with system
feedback (Operation, Sensors, etc.)
- Recommend to “Continue Mission” if it fits within
MTOD limit. If not, recommend “Return to Base”
Depot
- Prognostics provide predictions of drive system
and component lives and failure modes.
- Confirm predictions with system feedback
(Operation, Sensors, etc.)
- Focus on the components of interest and
repair/replace/inspect as needed (at aircraft
AVIM/AVUM level, at depot level)
Enterprise
- Prognostics provide predictions of drive system
component lives and failure modes.
- Confirm predictions with system feedback (CBM,
Sensors, etc.)
- Support design and development of new CBM
systems, condition indicators, and Deport
Maintenance Work Requirement (DMWR)
procedures
- Prognostics provide tools to assess health state
according to actual operational conditions.
- Use historic operational data to assess current
health state.
- Use data for inventory management, critical spare
parts etc.

18. Summary
6/9/2016
Prognostic Model for Rotorcraft Drive System
• A materials-based fatigue damage model has been developed for a gear health
algorithm solution for drive system design and operations.
• Using a rotorcraft planetary gear multibody dynamic model, contact and fillet stress
analyses were performed. A stochastic microstructure model was used to predict
planetary gear system fatigue life at nominal and overload conditions.
• Prognostic model was used to predict contact fatigue and bending fatigue life,
endurance limits, maximum continuous power (MCP) rating, and overload effects.
• Demonstrated prognostics integration with onboard and offboard elements of
industry health monitoring/management systems.
• Prognostic model results show the application of this gear health algorithm solution
in rotorcraft drive system transmission gear design, inspection, maintenance, and
recommendation of safe operational powers