NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Wind Turbine Drivetrain Condition 

Monitoring  An Overview 

Shuangwen (Shawn) Sheng, 

NREL 

 

Wenxian Yang, 

University of Newcastle 

ASME Turbo Expo 2013

June 5, 2013

San Antonio, TX

NREL/PR-5000-58774 Photo by Dennis Schroeder, NREL 19018
 NATIONAL RENEWABLE ENERGY LABORATORY

Agenda 

 Introduction

 

 Drivetrain Condition Monitoring 

(CM)

 Case Studies and Discussions 

 Concluding Remarks

2

U.S. Department of Energy 1.5 -MW wind turbine, Photo by Lee Jay 

Fingersh, NREL 17245 
 NATIONAL RENEWABLE ENERGY LABORATORY

Introduction 

3

 Global Wind Energy

 Wind Turbine Gearbox Reliability 

Challenge

 Gearbox Reliability Collaborative

 Benefits of Condition Monitoring

 Operation and Maintenance of Wind 

Plants
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Global Wind Energy [1]

4

Photo by HC Sorenson, NREL 17856

Photo by Iberdrola Renewables, Inc., NREL 16706 
 NATIONAL RENEWABLE ENERGY LABORATORY

Global Wind Energy (Continued) [1]

5
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Reliability and Downtime of Turbine Subassemblies [2,3] 

6

Drive Train

Generator

Gearbox

Rotor Blades

Mechanical Brake

Rotor Hub

Yaw System

Hydraulic System

Other

Electrical Control

Electrical System LWK Failure Rate, approx 5800 Turbine Years

WMEP Failure Rate, approx 15400 Turbine Years

LWK Downtime, approx 5800 Turbine Years

WMEP Downtime, approx 15400 Turbine Years

1 0.75 0.5 0.25 0 2 4 6 8 10 12 14 

Failure/turbine/year Downtime per failure (days)

Failure/turbine/year and downtime from two large surveys of land-based European wind turbines over 13 years

• WMEP: the Wissenschaftliches Mess- und Evaluierungsprogramm (WMEP) database was 

accomplished from 1989 to 2006 and contains failure statistics from 1,500 wind turbines. 

• LWK: failure statistics published by Landwirtschaftskammer Schleswig-Holstein (LWK) 

from 1993 to 2006. It contains failure data from more than 650 wind turbines. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Failure Modes of Wind Turbine Gearboxes

7

Gearbox Component Failure Modes

Gear case Fracture 

Suspension Wear, looseness

Torque arm Wear, looseness 

Lubrication system Loss of lubricant, contaminated lubricant, aged lubricant, lubricant system 

failure, lubricant pump failure, blocked lubrication filters, blocked jets

Epicyclic part - planet carrier Lubrication

Epicyclic part – planet 

bearing Bearing failure, lubrication

Epicyclic part – planet gear Tooth failure, lubrication

Epicyclic part – internal gear Tooth failure, lubrication, fracture

Epicyclic part – sun gear Tooth failure, lubrication

Epicyclic part - shaft Cracking, journal damage

Parallel shaft part - gear Tooth failure, lubrication

Parallel shaft part - bearing Bearing failure, lubrication

Parallel shaft part - pinion Tooth failure, lubrication

Parallel shaft part - shaft Cracking, journal damage

High-speed shaft Cracking, permanent bend

 Gearboxes do not always achieve their 20-year design lifetime

Illustration by NREL
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Impact of Gearbox Failures

8

 Premature failure of gearboxes increases the cost of 

energy and may include:

• Turbine downtime

• Unplanned maintenance 

• Gearbox replacement and rebuild

• Increased warranty reserves

 The problem:

• Is widespread

• Affects most original equipment manufacturers

• Is not caused by manufacturing practices

Need an industry-wide solution, but ...
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Gearbox Reliability Collaborative (GRC)

9

 Facilitate dialog among all parties

• Designers and consultants

• Suppliers and rebuilders

• Operation and maintenance organizations

 Understand gearbox response to specific loading

• Pure torque, bending, and thrust (dynamometer)

• Turbulence (field)

 Understand the physics of premature wind turbine 

gearbox failure

 Identify gaps in the design process 

 Suggest improvements in design practices and 

analytical tools 
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Gearbox Reliability Collaborative (Continued)

10

 Technical approach

• Modeling and analysis 

• Field test 

• Dynamometer test 

• Failure database 

• Condition monitoring 

(CM)

 Goal 

• To improve gearbox 

reliability and 

increase turbine 

availability, which will 

reduce the cost of 

energy 

Field Test Dynamometer Test

• Test plan

• Test article

• Test setup and execution

• Test plan

• Test turbine

• Test setup and execution

Analysis

• Load cases

• System loads

• Internal loads

Test Turbine at NREL/Photo by Dennis Schroeder, NREL 19022

NREL dynamometer/Photo by Lee Jay 

Fingersh, NREL 16913 

Illustration by NREL
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Benefits of Condition Monitoring

11

 Early deterioration detection to 

avoid catastrophic failure

 Accurate damage evaluation to 

enable cost-effective maintenance 

practices (proactive instead of 

reactive)

 Increase turbine availability and 

reduce operation and 

maintenance costs

 Root cause analysis to 

recommend improvements in 

component design or equipment 

operation and control strategies 

Test turbine at Ponnequin/Photo by Jeroen van Dam, NREL 19257
 NATIONAL RENEWABLE ENERGY LABORATORY

Operation and Maintenance of Wind Plants

12

 Operation and 

maintenance (O&M) 

research needs: 

• A globally installed capacity 

of ~280 gigawatts (GW); 

majority of which are out of 

warranty 

• A 1% performance 

improvement: ~$88.2 billion 

additional revenue 

[assumed: 30% capacity 

factor, $120/megawatt-hour 

(MWh) electricity rate]

• Extremely high replacement 

costs for most subsystems

• Example replacement costs for a 

5-megawatt (MW) wind turbine 

[4]:

 For a rotor: $1.9−$2.3 million

 For a blade: $391,000−$547,000 

 For a blade bearing: $62,500−$78,200

 For a gearbox: $628,000 

 For a generator: $314,000

 For electronic modules: $16,000 

Land-based wind plant /Photo by David Hicks, NREL 18453
 NATIONAL RENEWABLE ENERGY LABORATORY

O&M of Wind Plants (Continued) 

13

 O&M cost reduction 

opportunity: 

• Is ~21% for offshore plants

• Is ~11% for land-based plants

• Could be further reduced if 

O&M practices are improved, 

by:

 Considering performance 

monitoring for operation 

 Introducing condition-based 

maintenance

 And so on... 

 CM is an enabling technique 

with significant opportunity in 

offshore plants because of 

accessibility challenges

Estimated life cycle cost breakdown for a 

baseline offshore wind project [5]
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Drivetrain Condition Monitoring 

14

 Downtime caused by turbine subsystems

 Typical drivetrain CM practices 

Wind farm/Photo by Iberdrola Renewables, Inc., NREL 16707
 NATIONAL RENEWABLE ENERGY LABORATORY

Downtime Caused by Subsystems

15

 Data source: Wind Stats Newsletter, Vol. 16, Issue 1 to Vol. 22 Issue 4, 

covering 2003 to 2009 [6]

 Based on the data reported to Wind Stats for the first quarter of 2010, the 

data represents about 27,000 turbines, ranging from 500 kW to 5 MW

 Top three: 

1. Gearbox

2. Generator

3. Electric Systems

 Consider crane cost: 

• Main bearing also needs 

attention

• Electric systems often do 

not need an expensive 

crane rental 

Downtime caused by turbine subsystems [7]
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Typical Drivetrain CM Practices 

16

 Techniques 

• Supervisory control and 

data acquisition (SCADA) 

data

• Acoustic emission (e.g., 

stress wave) analysis 

• Vibration analysis

• Oil or grease analysis 

• Filter element 

• Electric signature

 Real-time continuous or 

offline periodic 

 One or a combination of 

several 

Sample vibration spectra [8]

Sample oil debris counts [8] 
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Case Studies and Discussions

17

 A 600-kilowatt (kW) test turbine 

• SCADA data

 A 750-kW test gearbox

• Stress wave analysis

• Vibration analysis 

• Oil debris monitoring

• Oil condition monitoring 

• Oil sample analysis 

 A 1.5-MW test turbine

• Filter element analysis

 A 30-kW test rig

• Electric signature analysis 

Wind turbines at the National Wind Technology Center, 

Photo by Dennis Schroeder, NREL 21928
 NATIONAL RENEWABLE ENERGY LABORATORY

SCADA Data

18

 A 600-kW test turbine

experienced gearbox damage and 

replacement

 SCADA data: 

• Readily available and no need of 

investment on dedicated CM 

systems

• Beneficial for identifying outliers by 

looking at key performance 

parameters, e.g., power

• Temperature channels may be used 

for CM of main bearings, generator 

bearings, and gearbox high-speed 

stage bearings, and so on 

• Not straightforward in pinpointing 

exact damaged 

subsystems/components

Torque to high-speed shaft speed ratio vs. power [9]

Q-Stat and T^2 values for baseline and fault cases [9]
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A 750-kW Test Gearbox 

19

 

1. Completed dynamometer run-in test

2. Sent for field test: experienced two oil losses 

3. Stopped field test 

4. Retested in the dynamometer under controlled conditions

High-speed stage gear damage

Photo by Lee Jay Fingersh, NREL 16913 Photo by Robert Errichello, NREL 19599
 NATIONAL RENEWABLE ENERGY LABORATORY

Stress Wave Analysis [10] 

20

 Dynamometer retest of the 

damaged gearbox (right) 

indicated abnormal gearbox 

behavior: distorted 

distribution and relatively 

higher amplitudes 

 Parallel stages sensor

 Stress wave amplitude 

histogram [11]

 Dynamometer test of a 

reference gearbox of the 

same design (left) indicates 

healthy gearbox behavior
 NATIONAL RENEWABLE ENERGY LABORATORY

Vibration Analysis [10] 

21

 Intermediate speed shaft 

sensor

 Dynamometer test of the 

same reference gearbox 

(left) indicated healthy 

gearbox behavior

 Dynamometer retest of the 

damaged gearbox (right) 

indicated abnormal behavior 

• More side band frequencies

• Elevated gear meshing 

frequency amplitudes
 NATIONAL RENEWABLE ENERGY LABORATORY

Oil Debris Monitoring [10]

22

0

100

200

300

400

500

600

700

800

9/15 9/15 9/16 9/16 9/17 9/17 9/18

Particle Counts

Date

 Particle generation rates:

• Damaged test gearbox: 70 particles/hour on 9/16/2010

• Healthy reference gearbox: 11 particles over a period of 4 hours
 NATIONAL RENEWABLE ENERGY LABORATORY

Oil Condition Monitoring (Continued)

23

 Field test of the

test gearbox (left):

• Wild dynamics

• Possible damage

 Dynamometer retest 

(right): 

• Well-controlled test 

conditions

• Possible damage
 NATIONAL RENEWABLE ENERGY LABORATORY

Oil Sample Analysis [12] 

24

 Results: dynamometer test of the reference gearbox

• Particle counts: important to identify particle types

Reference Limits Analysis Results

• Element identification
 NATIONAL RENEWABLE ENERGY LABORATORY

A 1.5-MW Test Turbine [13] 

25

 U.S. Department of 

Energy (DOE) 1.5-MW GE 

Turbine

• Model: GE 1.5 SLE 

• Tower height: 80 m 

• Rotor diameter: 77 m 

• Located at the National 

Wind Technology Center 

at the National Renewable 

Energy Laboratory in 

Colorado 

• DOE-owned 

• Used for research and 

education Test wind turbine, Photo by Dennis 

Schroeder, NREL 20375
 NATIONAL RENEWABLE ENERGY LABORATORY

Filter Element Analysis [13] 

26

Element Brass Zinc FeO Steel Total

Cr 0.0 0.1 0.0 2.2 0.2

Mn 0.1 0.1 0.3 0.1 0.1

Fe 0.8 2.9 59.9 81.8 8.9

Co 0.1 0.5 0.3 0.6 0.2

Ni 0.3 0.5 0.7 0.9 0.4

Cu 46.6 4.5 1.5 1.7 39.6

Zn 38.5 57.5 3.6 5.4 35.1

Classification Rule 

Barium Ba >40%

Brass Cu + Zn >40% and Cu >10%

Zinc Zn >40%

Iron Oxide Fe >30% and O >15%

Steel Fe >30%

Additives S + P + Zn >50%

Silicates Si >5%

Miscellaneous All remaining particles

 Direct reading Ferrography 

results normal 

 Important spectrometer results 

(ppm): Fe = 22, Cu = 36, Zn = 

1621 (Additive masks alloy) 

• Hard to conclude the 

debris includes brass and 

steel

 Filter element analysis

• Indicated high level of 

brass and steel 

• Uncovered what might not 

be detectable by a 

conventional oil sample 

analysis 

Classification Rules

Main Loop Filter Element Analysis
 NATIONAL RENEWABLE ENERGY LABORATORY

Electric Signature Analysis 

27

 Did not reveal many fault signatures in 

the 750-kW damaged gearbox test

 In literature, electric signature analysis 

has mainly been explored based on 

generator test rigs or small-scale wind 

turbines for generators CM [14]

• Power signals appeared more effective 

than either currents or voltages [15] 

• Capable of detecting both mechanical and 

electrical faults seen in generators [15]

• Not considered to be a suitable alternative 

to vibration monitoring in geared wind 

turbines [16]

 May have potential for direct-drive 

wind turbine drivetrain CM, as 

generators will replace gearboxes to 

become the critical component

-0.05

0

0.05

currents (kA)

-0.5

0

0.5

voltages (kV)

0 20 40 60 80 100

-3

-2

-1

0

power (KW)

time (s)

normal abnormal normal abnormal normal

82

84

86

88

δ (degree)

0

0.01

0.02

r

0 20 40 60 80 100

4

6

8

e (%)

time (s)

normal abnormal normal abnormal normal

Electric signals collected from a 30-kW generator test 

rig with simulated winding asymmetry fault [4]

Faults indicated by three different CM criteria [4]
 NATIONAL RENEWABLE ENERGY LABORATORY

Discussions

28

 SCADA data are beneficial for identifying abnormal 

turbines by tracking key performance parameters, but are 

limited when carrying out a full condition monitoring of 

wind turbine subsystems/components. 

 Temperature channels may be used for the CM of main 

bearings, generator bearings, or gearbox high-speed 

stage bearings, and so on. 

 Stress wave analysis (amplitude histogram) appears 

effective for detecting gearbox abnormal health conditions, 

but the sensors may be prone to environmental noises.

 Spectrum analysis of the vibration signal (or stress waves) 

can, to a certain extent, pinpoint the location of damaged 

gearbox components, but may have challenges with lowspeed components, such as gearbox planet stage 

bearings or main bearings. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Discussions (Continued)

29

 Oil debris monitoring, specifically particle counts, is 

effective for monitoring gearbox component damage, but 

not effective for pinpointing damage locations 

 Damaged gearbox releases particles at increased rates

 Oil condition monitoring, specifically moisture, total 

ferrous debris, and oil quality:

• Oil total ferrous debris appears indicative for gearbox 

component damage 

• More data is required to understand oil moisture and quality

 When obtaining particle counts through oil sample 

analysis, attention should be given to identifying particle 

types 

 Periodic oil sample analysis may help pinpoint failed 

component and root cause analysis 
 NATIONAL RENEWABLE ENERGY LABORATORY

Discussions (Continued)

30

 Filter element analysis may reveal what is typically 

missed in conventional oil sample analysis 

 Electric current analysis appears effective for generator 

mechanical and electric fault detections:

• Only verified on laboratory test rigs or small-scale wind 

turbines thus far

• Some validations on utility-scale wind turbines are needed 

before the technique can have a bigger impact 

• Direct-drive wind turbines may present a good opportunity for 

electric signature analysis 

 Given the diverse and complex failure modes seen in 

wind turbine drivetrains, an integration approach is 

recommended, starting with an initial digest of SCADA 

data and then fusing several dedicated techniques by 

considering their advantages and disadvantages. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Concluding Remarks

31

 Challenges

 Future research and development areas

Offshore wind turbine/Photo by Eric Nelson, NREL 21965
 NATIONAL RENEWABLE ENERGY LABORATORY

Challenges [17]

32

 Justification of cost benefits for CM: each wind 

turbine has a relatively lower revenue stream than 

traditional power generation and site variations 

 Limited machine accessibility: makes retrofitting of 

CM systems or taking oil/grease samples difficult

 Cost-effective and universal measurement strategy: 

sensor readings are affected by mounting locations 

and various drivetrain and gearbox configurations 

 Diagnostics: variable-speed and load conditions and 

very low rotor speeds challenge traditional diagnostic 

techniques developed for other applications 
 NATIONAL RENEWABLE ENERGY LABORATORY

Challenges (Continued) [17]

33

 Data interpretation for both SCADA and 

dedicated CM systems: requires expert 

assistance for data analysis and maintenance 

recommendations

 Oil sample analysis: sample variations, different 

lubricant may require different sets of tests or 

procedures 

 Additional complexity for offshore: foundation, 

undersea transmission lines, saltwater and wave 

influences on turbine, and weather forecast 

 The limitations of existing industry standards in 

the application to wind industry 
 NATIONAL RENEWABLE ENERGY LABORATORY

Future Research and Development Areas [17]

34

 Research on improved use of SCADA data

 Determine cost-effective monitoring strategy

 Improve accuracy and reliability of diagnostic decisions, 

including level of severity evaluation 

 Automate data interpretation to deliver actionable 

maintenance recommendations

 Develop reliable and accurate prognostic techniques

 Research fleet-wide condition monitoring and asset 

management 

 Improve turbine operation, control strategy, and 

component design through root cause analysis 

Challenging yet rewarding
 NATIONAL RENEWABLE ENERGY LABORATORY

References

35

1. Global Wind Energy Council. (2012). Global Wind Report Annual Market Update 2012. 

http://www.gwec.net/wp-content/uploads/2012/06/Annual_report_2012_LowRes.pdf. 

[Accessed 05/15/2013].

2. Feng, Y.; Tavner, P.; (2010). Introduction to Wind Turbines and Their Reliability & Availability, 

presented at the European Wind Energy Conference, April 20-23, Warsaw, Poland. 

3. Tavner, P.; Spinato, F.; van Bussel, G.J.W.; Koutoulakos, E. (2008). Reliability of Different 

Wind Turbine Concepts with Relevance to Offshore Application, presented at the European 

Wind Energy Conference, March 31 – April 3, Brussels, Belgium. 

4. Yang W.; Sheng S.; Court, R. (2012). Operational-Condition-Independent Criteria Dedicated 

to Monitoring Wind Turbine Generators: Preprint. 9 pp.; NREL Report No. CP-5000-55195. 

5. Meadows R. (2012). “Offshore Wind O&M Challenges,” Wind Turbine Condition Monitoring 

Workshop, Broomfield, CO, September 19−21, 2011. 

6. Wind Stats Newsletter. (2003–2009). Vol. 16, No. 1 to Vol. 22, No. 4, Haymarket Business 

Media, London, UK.

7. Sheng S.; Link H.; LaCava W.; van Dam J.; McNiff B.; Veers P.; Keller J.; Butterfield S.; 

Oyague F. (2011). “Wind Turbine Drivetrain Condition Monitoring During GRC Phase 1 and 

Phase 2 Testing,” 58 pp., NREL Report No. TP-5000-52748.

8. Dempsey, P.; Sheng, S. (2011). “Investigation of Data Fusion for Health Monitoring of Wind 

Turbine Drivetrain Components,” presented at the 2011 American Wind Energy Association 

WINDPOWER Conference, Anaheim, CA, USA, May 22−25, 2011.

9. Parthasarathy G.; Kim K.; Foslien W.; Uluyol O.; Sheng S.; Fleming P. (2011). “Use of SCADA 

Data for Failure Detection in Wind Turbines,” published in the 2011 Energy Sustainability 

Conference and Fuel Cell Conference Proceedings, Washington DC, USA, August 7−10, 

2011. 
 NATIONAL RENEWABLE ENERGY LABORATORY

References (Continued)

36

10. Sheng, S. (2011). “Investigation of Various Condition Monitoring Techniques Based on a 

Damaged Wind Turbine Gearbox,” 8th International Workshop on Structural Health 

Monitoring 2011 Proceedings, Stanford, CA, September 13-15, 2011. 

11. Board, D. B. (2003). “Stress Wave Analysis Provides Early Detection of Lubrication 

Problems,” Practicing Oil Analysis magazine. 

12. Sheng, S. (2011). “Investigation of Oil Conditioning, Real-time Monitoring and Oil Sample 

Analysis for Wind Turbine Gearboxes,” presented at the 2011 AWEA Project Performance 

and Reliability Workshop, January 12–13, 2011, San Diego, CA.

13. Sheng S.; Herguth, W.; Roberts D. (2013). “Condition Monitoring of Wind Turbine 

Gearboxes Through Compact Filter Element Analysis,” presented at the 2013 Society of 

Tribologists and Lubrication Engineers Annual Meeting and Exhibition, Detroit, MI, USA, 

May 6−9, 2013. 

14. Gong, X. (2012). Online Nonintrusive Condition Monitoring and Fault Detection for Wind 

Turbines. Dissertation. University of Nebraska Lincoln, August 2012. 

15. Yang W.; Tavner P.; Crabtree C.; Wilkinson M. (2010). “Cost Effective Condition Monitoring 

for Wind Turbines,” IEEE Transactions on Industrial Electronics, Vo. 57, No. 1, pp. 

263−271. 

16. Madsen, B. (2011). Condition Monitoring of Wind Turbines by Electric Signature Analysis. 

Thesis. Technical University of Denmark, October 2011. 

17. Sheng, S.; Veers, P. (2011). “Wind Turbine Drivetrain Condition Monitoring – An 

Overview,” Machinery Failure Prevention Technology (MFPT) Society 2011 Conference 

Proceedings, Virginia Beach, VA, May 10−12, 2011. 
 NATIONAL RENEWABLE ENERGY LABORATORY

HC Sorensen, Middelgrunden Wind Turbine 

Cooperative, Photo by HC Sorensen, NREL 17855 

Thanks for Your Attention!

37

Special thanks go to DOE and the condition 

monitoring research partners! 

shuangwen.sheng@nrel.gov 

303-384-7106