PowerPoint Presentation

GEAR HEALTH ALGORITHM SOLUTIONFOR DRIVE SYSTEM DESIGN AND OPERATIONSRaja V. PulikolluSentient Science

AHS Conference, Forum 726/9/16

Acknowledgements6/9/16Prognostic Model for Rotorcraft Drive System U.S. Army Aviation Applied Technology Directorate (AATD)Bruce Thompson, Treven Baker, Clay Ames, Matt Spies

The Boeing CompanyTony Shen, Doug Knapp, Steve Slaughter, Alice Murphy

Applying Material Science & Computational Testing to Determine Component & System Failure Rates

Introducing Sentient SciencePrognostic Model for Rotorcraft Drive System

2001-201620102014April 20166/9/16

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Army Vision for Prognostics6/9/16Prognostic 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 LabProposed ModelThis paper presents physics-based models as a key component of prognostic and diagnostic algorithms of health monitoring systems.

Overall Objectives6/9/16Prognostic 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

Drives System Rating and Life CalculationConventional Approach6/9/16Prognostic Model for Rotorcraft Drive System

AGMA provides notional deterministic, conservative fatigue life curve based on empirical factors

Conventional approach doesnt 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)

Drives System Rating and Life CalculationMaterials-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 Steps4. Simulate Stress in Microstructure to Predict Crack Initiation & Propagation6. Report on Damage Mode and Fatigue Life Distribution

Prognostic Model for Rotorcraft Drive System 6/9/16

Planetary Gear System Multi-Body Dynamic Model For Load Analysis6/9/16Prognostic Model for Rotorcraft Drive System

Created mesh stiffness using Abaqus modelCreated bearing stiffness matrix using bearing geometry details Created system model using assembly dimension and relative position from step file

6/9/16Prognostic 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 gearboxDeveloped computational models of different components in planetary systemAnalyzed stresses translated from system/component loadsDetermined high stress regions of sun gear, component of interest

Stochastic Microstructure Model Sun Gear6/9/16Prognostic 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

Mixed-EHL Model For Sun Gear Contact Stress Analysis6/9/16Prognostic 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 tractionsOutcome: Determine the performance of a given surface finish during the generation, sustainment, and/or failure of an EHL film at the contact zone.

SampleRMS Sq (in)Average Sa (in)Skewness SskKurtosis SkuAMS 63081411-0.3416AMS 62651411-1.134

Mobil AGL properties, input to mixed-EHL model:Operating Temperature200FAbsolute Viscosity 0.009648kg/(m s)Pressure Viscosity Coefficient9.5e-9/Pascal

6/9/16Prognostic Model for Rotorcraft Drive System Fatigue Life ModelingSun Gear Bending Fatigue at Overload Conditions

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

6/9/16Prognostic Model for Rotorcraft Drive System Fatigue Life ModelingSun 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

Fatigue Life ModelingSun Gear Contact Fatigue at Overload Conditions 6/9/16Prognostic Model for Rotorcraft Drive System Max contact Pressure at Overload: 253.7 KsiPitting is the dominant damage mode. Crack initiation location: 10 and 120m into the depthDigitalClone calculates micro-stress response of the material to the applied loading/traction

Contact SurfaceContact SurfaceCrack initiation depths: 10 um and 120.82umPit Width: 76.46umPit Depth: 201.16umLife (rev): 5.41E+07Towards TipTowards Root

Surface material loss

AMS 6308 subsurface microstructure

6/9/16Prognostic Model for Rotorcraft Drive System Fatigue Life ModelingSun Gear Contact Fatigue at Overload Conditions

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

Prognostic Model Integration and Demonstration For Autonomous Monitoring6/9/16Prognostic Model for Rotorcraft Drive System 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 UPDATEThe 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

6/9/16Prognostic Model for Rotorcraft Drive System Prognostic Model Integration and Demonstration For Autonomous MonitoringLevelEventActionsPilot- 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 BaseDepot- 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.

Summary6/9/16Prognostic 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