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DigitalClone® Wind Turbine Validations
The document outlines Sentient Science's digitalclone® technology, which utilizes a 'small data' approach to predict component failures in wind turbines, allowing operators to anticipate issues and extend the life of critical components. It emphasizes the financial benefits of this technology, including significant maintenance cost reductions and informed operational decisions. Additionally, it provides validation data correlating predictions with actual failures and improvements gained through the implementation of these predictive models.
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DigitalClone® Wind Turbine Validations
- 1. © 2016 SentientScience Corporation – Confidential & Proprietary DigitalClone® Wind Turbine Validations
- 2. DigitalClone® is a“Small Data” Approach A “small data” approach to determine component and system failure rates DigitalClone® Wind Validations 2 • When bearings enter failure, it happens very fast... minutes, hours, days • That fast failure causes failure in other expensive components • Save the bearing and you get life extension and a larger pay back than Big Data
- 3. “Small Data” Approach Operatorsand suppliers will know the earliest point in time when critical components will begin to fail and have options to extend life DigitalClone® Wind Validations 3
- 4. “Small Data” Approach Thismeans you now have much more information on asset life, life extension, financial impact DigitalClone® Wind Validations Earliest Failure Reporting & Actions Known Component Failure Accurate Financial Planning • 18 month Rolling O&M Forecasting • CBM Optimization• Risk Management 4
- 5. © 2016 SentientScience Corporation – Confidential & Proprietary Aligning with Our Customers Business Goals DigitalClone® Wind Validations 5 Operations Management Asset Management Risk Management New Development Reduce Energy Costs by 13% or $10/MWH Supply Chain Business Strategy 6 Areas of Business Impact, Value and Focus
- 6. Overview of Sentient’s6-step Technology Process DigitalClone® Wind Validations 6 Calculate Component Stresses (bulk) Build Material Microstructure Model Build Surface Model Calculate Probabilistic Life Predict Failure Mode & Component Life Simulate Material Microstructure Response Subsurface crack network Contact Surface Sub-surface Ground Finish Superfinish 2.1. 3. 4.5.6.
- 7. 3 Life ExtensionServices 1. DigitalClone® • Materials-Based Computational Testing (SaaS) 2. DigitalClone® Live for Operators • Life Extension of Gearboxes, Main bearings, Pitch Bearings 3. DigitalClone® Live for Suppliers • Interfacing Suppliers with Operators DigitalClone® Wind Validations 7
- 8. © 2016 SentientScience Corporation – Confidential & Proprietary Sentient Predicted Failed and Damaged Gearboxes Correlated well with Field Data Wind Turbine Gearbox Validation Sentient Predicted Failed and Damaged Gearboxes Correlated well with Field Data DigitalClone® Wind Validations 8 Turbine ID Sentient Predictions Actual/Site Info Sentient Recommendation Notes A Mech. failure Nov 2012 Replaced March 2013 N/A Predictions line up perfectly with actual site data B Mech. failure next 6 months On watch list Derate to 1.75MW Sentient recommends turbine be inspected for main bearing pitting/spalling, derate to push back failure 1-2 months C Mech. failure Aug 2012 Replaced Sept. 2012 N/A Predictions line up perfectly with actual site data D Mech. failure any moment On watch list Derate to 1.5MW Sentient recommends turbine be inspected for main bearing pitting/spalling, derate to push back failure 1-2 months Sentient Science Solution 1. Reduce the cost of maintenance by transforming CM to PM through Optimization. 2. $50-$55 MM of Maintenance Cost reduction potential in 5 years. Business Problem 1. CM & PM mix as of today is 55:45 respectively. It will only get worse with aging. 2. CM costs 30X more than PM. CM cost to go up with aging.
- 9. © 2016 SentientScience Corporation – Confidential & Proprietary Wind Turbine Gearbox Validation Predicted 38 Gearboxes with Damage, Confirmed by Site Inspections DigitalClone® Wind Validations 9 DigitalClone® Fleet Failure Data Customer knew of 18 critical assets DigitalClone® predicted an additional 20 Customer was able to plan maintenance with little to no unscheduled downtime over next two years → Parts on hand for replacements on site exactly when needed, no delays → No failures caused immediate downtime → Unplanned failures were prevented, customer saved over $4.7M in first year Reduced failure risk
- 10. © 2016 SentientScience Corporation – Confidential & Proprietary Gearbox Life Extension Validation Uptower and Derate Recommendations DigitalClone® Wind Validations 10 → Sentient provided Watch List and Life Extension Action reports to customer during Dec. 2014 → Long term and short term visibility to failure, uptower repair was made during scheduled maintenance activity, and zero unplanned downtime was incurred. → Traditional data analytics did not detect this failure Butterfly Wings Turbine ID DigitalClone Predicted MTOD Damage Noticed Date Action Taken for Life Extension Life Extension, Months A Sept, 2015 Oct, 2015 Uptower Replacement 33 B Oct, 2016 Aug, 2016 Derate 14
- 11. © 2016 SentientScience Corporation – Confidential & Proprietary 1.5 MW Fleet Failure Rate Predictions Validation GBX Supplier Differentiation and Validation → DigitalClone® prognostic modeling applied to the 1.5 MW gearboxes → DigitalClone® predicted cumulative fleet failure rate correlated well with customer’s failure data → Individual years fleet failure rate and O&M costs predicted in the next 1 to 5 years → Operators take actions to reduce this O&M cost increase with dynamic models to enable turbine life extension from: 1. Optimizing Uptower Component Replacements 2. Gearbox Upgrades and Operational Changes (Lubricants, Derate/Uprate) 3. Major Component Long Lead Time Planning 4. Scheduled versus Unscheduled Maintenance 5. Evaluate New Gearbox and Components Life Extension before Procurement DigitalClone® Wind Validations 11 Supplier 1 Supplier 2 Supplier 3 Supplier 4 5 years 10 years 15 years 20 years Customers Cumulative Fleet Failure Rate as of 2016
- 12. © 2016 SentientScience Corporation – Confidential & Proprietary DigitalClone®Simulation Bearing Supplier Hardness (Ra) Range 0 to 4500 um depth Microstructure Inclusions Microstructure Quality D 60–58 HRC A 60-53 HRC (Design requirement >58 HRC) 1.5 MW Gearbox Bearing Supplier Quality Prediction and Validation Differences in Bearing Suppliers Identified and Validated with Site Failures DigitalClone® Wind Validations 12 HSS Bearing DigitalClone® Comparison Stress / Life Curve → Sentient predicted Supplier A HSS bearing failures within 8 years of operation. 100% of site’s HSS bearing failures are with Supplier A, confirming DigitalClone® predictions. → Bearing material microstructure, surface finish, and material quality impact life…depending upon location and stresses experienced. BESTBESTBEST Cycles to Damage (L66) MaxContactPressure(MPa) Stress – Life Curve A Better Life BetterStressPerformance D BEST
- 13. © 2016 SentientScience Corporation – Confidential & Proprietary Component Supplier Predicted L10 Life Planet Bearing Supplier A 8 yrs Supplier C 15 yrs Supplier D > 20 yrs Low Speed Intermediate Shaft Bearing UW Supplier A 6.1 yrs Low Speed Intermediate Shaft Bearing DW Supplier A 8.1 yrs High Speed Intermediate Shaft Bearing UW Supplier E > 20 yrs Supplier C 14 yrs High Speed Intermediate Shaft Bearing DW Supplier E > 20 yrs Supplier B 12.2 yrs High Speed Shaft Bearing UW Supplier E > 20 yrs Supplier C > 20 yrs Supplier B 9 yrs High Speed Shaft Bearing DW Supplier E > 20 yrs Supplier C > 20 yrs Supplier B 7.7 yrs Supplier D 12.5 yrs Risk ranking of bearing suppliers, confirmed by Customer’s site failures and teardown reports • Supplier A (highest risk) • Supplier B • Supplier C • Supplier D • Supplier E (lowest risk) Validation - Effect of Bearing Supplier Quality on 1.5 MW Gearbox Life DigitalClone® Wind Validations 13 Primary Component Risk: 1. High speed shaft (HSS) bearing 2. Planet bearing 3. Low Speed Inter. Shaft (LSIS) bearing 4. High Speed Inter. Shaft (HSIS) bearing Secondary Component Risk: 1. High Speed Shaft (HSS) Pinion and Inter. Speed Shaft 2. Planetary Gear/Sun Gear 3. Low Speed Shaft and Inter. Gear 4. Ring Gear Misalignment and bearing pitting / spalling then causes…
- 14. © 2016 SentientScience Corporation – Confidential & Proprietary Validation - Effect of Gear Supplier Quality on Gearbox Life DigitalClone® Wind Validations 14 Business Problem 1. Need for advanced gear materials to meet new gearbox design requirements. 2. It is expensive and time consuming to evaluate gearbox performance at various operating conditions and life accurately Validation & Sentient Science Solution 1. Sentient predicted the effect of gear material quality on performance, validated with physical test data 2. 1000x more virtual test points for gears at reduced time and cost over physical test 3. Combining statistically significant virtual test points with limited physical test data provides accurate fatigue life estimates and allowable stress/loads 4. Simulate the affect on gearbox life from: 1. Overload/over torque conditions 2. Manufacturing Process 3. Lubrication 4. Residual stress 5. Surface finish Case Core Gear Tooth
- 15. © 2016 SentientScience Corporation – Confidential & Proprietary Critical Wear Location: Azimuthal Angle of 207 𝑜 at - 74.17 mm 6MW Turbine Main Bearing Validation Sentient conducted RCA and predicted Main Bearing Damage Location, correlating with field failures DigitalClone® Wind Validations 15 → Sentient used DigitalClone® to predict the effect of site operating conditions on main bearing fatigue life and conducted RCA for premature bearing failures → DigitalClone® predicted adhesive/abrasive wear (lubricant starvation) on the outer race as the primary damage mode leading to main bearing failure, confirmed by site observations Wear Bands Predicted Actual
- 16. © 2016 SentientScience Corporation – Confidential & Proprietary Drives System Bearing Supplier Comparison and Validation DigitalClone® predicted that bearing Supplier D is superior compared to other suppliers, confirmed by physical test data DigitalClone® Wind Validations 16 • Bearing supplier ranking based on material quality and life. • Sentient predicted bearing life and ranking correlated with test data → OEM selected Supplier D for production Bearing A Version 1 Bearing A Version 2 Bearing B Poor Quality Bearing B Good Quality Bearing C Bearing D Supplier Ranking Supplier Test Data L10 Life, Hours Sentient Predictions L10 Life, Hours 2 Supplier A Ver1 & Ver 2 978.00 (Ver 1&2) 622.58 (Ver 1) 716.93 (Ver 1&2) 764.25 (Ver 2) 4 Supplier B with & without Carbides 308.00 (with and without Carbides) Without carbides - 653.90 370.26 (with and without Carbides) With carbides - 130.45 3 Supplier C 380.00 380.39 N/A 1 Supplier D 1056.0 835.93 N/A
- 17. © 2016 SentientScience Corporation – Confidential & Proprietary Test data Advanced Bearing Modeling and Validation Case Hardened vs. Shot Peened vs. Nitrided DigitalClone® Wind Validations 17 • Bearings OEM and Sentient Science began a computational testing and DigitalClone Supply initiative in 2015 to support bearing material upgrade initiatives. • OEM could not test the life of Bearing C (nitrided) to failure, cost and time was prohibitive. • Sentient evaluated 3 bearings with different material microstructures, residual stresses, and microgeometries. OEM selected nitrided bearings, validating DigitalClone® Value Assessment: → Replaced $650K+ Expense of Equivalent Bearing Fatigue Tests → Replaced 534 Days of Equivalent Bearing Fatigue Tests → Quantified Life Extension of the Bearing DigitalClone Physical Test Data L10 Life, Hrs L50 Life, Hrs L90 Life, Hrs Weibull Slope L10 Life, Hrs L50 Life, Hrs L90 Life, Hrs Weibull Slope Baseline 8.91 33.65 78.47 1.418 14.80 38.04 69.43 1.64 Peened 9.035 34.614 81.47 1.403 15.76 47.70 96.63 1.32 Nitrided 140.56 233.66 323.04 1.508 - - - -
Editor's Notes
- #3 Pedigree: This is the same process the military did with us. We were built from the ground up to work with the Technologies who manage the Death process. 30M investment from DoD, DoE Built for the worlds largest OEMs 10 years design and validation Introduced into wind 18000 assets under contarct Focused on making the operators sucessful Working with suppliers to match operator demand with supply
- #4 Bearings are unique to other components in that they have a realitvely long initiation of failure (What Sentient Sees) and a extremely short propagation of failure (What sensors see) When bearings propagate failure, unusable failure, it happens very fast, minutes, hours, days - if you are lucky. That fast failure causes failure in other expensive components. Save the bearing and you get life extension and a larger payback than big data. Therefore Big Data from Sensors like GE Predix and the plethora of Big Data offers, answers are too late for Life extension.