Turbine Life Prediction and Reliability: 

Design and Operation

Paul Veers, Shawn Sheng, Jon Keller, Yi Guo

National Renewable Energy Laboratory

IEA TEM #93, December 13, 2018

Technical University of Denmark
NREL | 2

Design and Operation

Life Extension

• In design: Move to greater economic life (30–40 years?) 

• In operation: Extend economic life beyond design estimates 

• Components to consider: Structural, mechanical and electrical

Reliability Assessment

• In design (inherent): Material properties, assumed loading spectrum, 

idealized operational conditions, damage models, etc.

– Mostly physics based: Data supported

• In operation: Impacted by load deviated from design, maintenance 

practices, environmental conditions, etc.

– Data-based analysis: Physics supported

Design Criteria vs. Experience

• Example: NREL’s Drivetrain Reliability Collaborative 

• Mechanical: Gearboxes and bearings
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Fatigue Analysis – Probability of Failure

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

Log (Residual Lifetime)

Inverse Normal

Residual

Normal Fit

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8

Cycles to Failure

Normalized Max Strain n 

Data

Log-Linear L-S Fit

95/95 Tolerance Limit

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8

Log (Cycles to Failure)

Normalized Max Strain

• Material variability is estimated with 

constant-amplitude tests.

• Variation in cycles to failure is fit to a 

known distribution.

• Standards require designing to an 

acceptable failure probability within 

a specified lifetime (e.g., 95/95).
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“Weibull” Curve:

Component Probability of Failure over Time

Probability of Failure

Time

Original 

Project Life

Extended 

Project Life

Statistical Update

• Actual failure rate is 

different than design 

probability of failure.

• Extrapolation can be 

based on experience.
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Present Value of Future 

Failures

• Probabilistic fatigue analysis has had little traction before now.

• It is difficult and often contains more simplification than many 

are willing to accept.

0.5

0.6

0.7

0.8

0.85

0.9

0.95

0.98

0.99

0.999

0.00

0.20

0.40

0.60

0.80

1.00

2 4 6 8 10 12 14 16 18 20

Survival 

Percentage

Probability

Years of Operation

0

0.1

0.2

0.3

0.4

0.5

2 4 6 8 10 12 14 16 18 20

Expected Present Value

Operation Years

Zero

0.05

0.09

Discount Rate

Component Probability of Failure Over Time

Present Value of Failure Cost

Source: Veers, P. S., .1996. “Fatigue Reliability of Wind Turbine Fleets: The Effect of Uncertainty on Projected Costs,” 

Journal of Solar Energy Engineering, Trans. of the ASME, Vol. 118, No. 4 (November). https://doi.org/10.1115/1.2871782.
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Assumptions

• The failures experienced in the operating plants 

are related to the failure modes addressed by 

the design requirements.

• As-manufactured material properties are well 

characterized.

• Loadings are consistent with the design criteria.

• Damage models are known and appropriate.

• Governed by a single damage mechanism.

• These are not always satisfied.
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Damage of Wind Turbine Gearboxes: Dominant 

Failure Mode Not Accounted for During Design

• Samples: ~1,100 records

– Bearings: 77%

– Gears: 17%

– Others: 6%

• Samples: ~80 records

– Axial cracks: 71%

– Spalling: 23%

– Pitting: 6%

Gearbox Failures by Subcomponent

Bearing Failure Causes
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The Challenge Is Not the Wind Industry Alone: 

Rolling Bearing Causes for Removal

Main failure mode 

targeted during design. 

Source: J. S. Cunningham, Jr. and M. A. Morgan, STLE Lubr. Engr., 1979
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Bearing Axial Cracking: Benchtop Tests

Source: Gould, B., and A. Greco. 2015. “The Influence of Sliding and Contact Severity on the 

Generation of White Etching Cracks.” doi.org/10.1007/s11249-015-0602-6.

3

2

E V Nt = ∆ μ

N

∆V

μN

Cumulative Frictional

Heat Energy
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High-Speed Bearing Analysis

Rated 1,500 kW Power

High Load, Low Sliding

250 kW Power

Low Load, Continuous Sliding

Cold operations at low power

generate more frictional energy than 

warm operations at high power. 

Toil = 

Tavg

Tavg

Tavg Tavg

Tavg

Source: Keller, J. and Y. Guo. 2018. High-Speed Shaft Bearing Roller Sliding Analysis. Golden, CO: 

National Renewable Energy Laboratory. NREL/TP-5000-72415.
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Frictional Energy Loss-Based Reliability Assessment 

Using Probability of Failure as the Metric

• Target dominant failure mode: high-speed-stage-bearing axial cracking 

• Frictional energy loss under both normal and transient operations: operation EEnnnn = ff μμ, NN, FF, ∆vv

• μμ: Friction coefficient

• NN: NNumber of cycles (roller)

• FF: Load

• ∆vv: Sliding velocity between roller/raceway

Transient

EEtttt = ff μμ,tt, FF, ∆vv

• μμ: Friction coefficient

• tt: Time

• FF: Load

• ∆vv: Sliding velocity between roller/raceway

Operation Transient

EE = EEnnnn + EEtttt

• First- and second-order reliability methods:

GG = ee∗ − (EEnnnn + EEtttt) PPff = PP GG UU < 0

Source: Guo, Yi and Shawn Sheng. 2018. Probability of Failure for Gearbox High-Speed-Stage Bearings in Wind Turbines. 

Golden CO: National Renewable Energy Laboratory. NREL/PR-5000-70972. https://www.nrel.gov/docs/fy18osti/70972.pdf.
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Reliability Assessment Probability of Failure, PPff, of 

Axial Cracking: Preliminary Results

• Some correlation between high 

energy with previous failures present.

• Turbine failures occurred two years 

earlier than the evaluation period 

that was available for analysis:

o Failure records yet to be updated.

• PPff quantifies failure risk for each 

turbine.

• PPff is a function of age, wind loads, 

turbine design, and operations.

Probability of Failure in A Plant

Annual Energy Accumulation
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Turbine or Plant Perspective 

Reliability Assessment and Prediction: 

Single Component

• Component failure mechanism

• Measurements 

• Damage analysis 

• Uncertainty quantification

Methodology Applied to Other Major 

Components 

• Structural or electrical 

• Dominant failure mode

• Damage model and failure criterion

• Probability of failure metric

Risk Analysis and Decision Making

• Risk is probability times consequence

• Economic analysis 

Photo by Iberdrola Renewables, Inc., NREL 16706 

Illustration by NREL

Photo from Gary Doll, 

University of Akron

0

2

4

6

8

2 6 10 14 18

Cumulative Cost 

Years of Operation

Expected (Average) Cost

90th Percentile Costs

50th Percentile Costs

Plant-Wide Cost Estimates

Source: Veers, P. S., .1996. “Fatigue Reliability of Wind Turbine Fleets: The Effect of Uncertainty on Projected 

Costs,” Journal of Solar Energy Engineering, Trans. of the ASME, Vol. 118, No. 4 (November). 

https://doi.org/10.1115/1.2871782.
www.nrel.gov

Thank You

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, 

LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the 

U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The 

views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. 

Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published 

form of this work, or allow others to do so, for U.S. Government purposes.

Special thanks go to the U.S. Department of Energy 

drivetrain reliability collaborative research partners! 

The Block Island Wind Farm—the first offshore wind farm in 

the United States. Photo by Dennis Schroeder, NREL 40389

NREL/PR-5000-73023