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.

Prognostics and Health Management of 

Wind Turbines: Current Status and Future 

Opportunities 

Shuangwen (Shawn) Sheng

National Renewable Energy 

Laboratory (NREL), U.S.A. 

Probabilistic Prognostics and 

Health Management of Energy 

Systems Workshop 

December 14-15, 2015

IIha Solteira, SP, Brazil

Photo by Dennis Schroeder, NREL 21883

NREL/PR-5000-65605
 NATIONAL RENEWABLE ENERGY LABORATORY

Outline

2

 Introduction 

 Current Status of Prognostics 

and Health Management in 

Wind Industry

 Research and Development 

(R&D) Activities

 Concluding Remarks 

Photo by Lee Jay Fingersh, NREL 17245
3

 Introduction 

 Global Wind Energy

 Wind Turbine Reliability Challenge

 Wind Plant Operation and Maintenance 

(O&M)

 Prognostics and Health Management

Illustration Credit: Jon Leather, Castrol (left) and NREL (right)
 NATIONAL RENEWABLE ENERGY LABORATORY

Global Wind Energy

4

Photo by HC Sorensen, Middelgrunden Wind Turbine Cooperative, 

NREL 17856 Photo by Iberdrola Renewables, Inc., NREL 16706 

Source: GWEC [1]
 NATIONAL RENEWABLE ENERGY LABORATORY

Reliability of Turbine Subassemblies: Old Statistics [2, 3] 

5

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

• The Wissenschaftliches Mess-und Evaluierungsprogramm (WMEP) database was 

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

• Failure statistics published by Landwirtschaftskammer Schleswig-Holstein (LWK) from 

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

0

2

4

6

Gearbox Generator Blades Mechanical Electrical Others 

Average Turbine Outage Duration 

for Failures >1 Hour (days)

<1MW

1 to 2 MW

Outage Duration for Different Subsystems: New Statistics [4]

6

 Downtime caused by premature component/subsystem 

failures, led by gearboxes, challenging the wind industry

and resulting in increased cost of energy for wind power 

- Mechanical: Yaw Systems, Mechanical 

Brakes, Hydraulic Systems, Rotor Hubs, 

Drivetrain

- Electrical: Sensors, Electrics, Control 

Systems
 NATIONAL RENEWABLE ENERGY LABORATORY

Wind Plant O&M

7

 O&M research needs: 

• The majority of the wind 

turbines (~370 gigawatts [GW]) 

installed worldwide are out of 

warranty 

• A 1% performance 

improvement: ~$1.2 billion 

additional revenue (assumed: 

30% capacity factor, 

$120/megawatt-hour [MWh] 

electricity rate)

• Extremely high replacement 

costs for most subsystems [5].

 O&M cost reduction and 

business opportunities: 

• ~21% of life cycle cost for 

offshore plants and ~11% 

for land-based plants [6]

• Further reductions 

achievable by improved 

O&M practices

• Global O&M market likely 

to reach $20.6 billion by 

2023. [7]

 Actions to improve 

performance, reliability, 

and availability more 

critical for offshore wind.
 NATIONAL RENEWABLE ENERGY LABORATORY

Prognostics and Health Management

8

 One definition of 

prognostics and health 

management [8]: an 

approach to system 

life-cycle support that 

seeks to reduce or 

eliminate inspections 

and time-based 

maintenance through 

accurate monitoring, 

incipient fault 

detection, and 

prediction of impending 

faults. One Architecture for Prognostics and Health 

Management Process [9]
 NATIONAL RENEWABLE ENERGY LABORATORY

Prognostics and Health Management (Cont.)

9

 Benefits [10]:

• Increased productivity

• Reduced downtime

• Reduced number and 

severity of failures, 

particularly 

unanticipated failures 

• Optimized operating 

performance

• Extended operating 

periods between 

maintenance

• Reduced 

unnecessary planned 

maintenance

• Reduced life-cycle 

cost.

 Applications:

• Fuel cell systems

• Nuclear power 

plants

• Aviation

• Electronics 

• Wind.
10

Current Status of Prognostics and Health Management in the Wind Industry

 Focuses

 Typical Practices 

• Performance Monitoring 

• Condition Monitoring.

Photo by Gary Norton/DOE, NREL 27358
11

Focuses

 Subsystems on drivetrain: main shaft bearing, 

gearbox, and generator 

 Layers 1 to 4: sensing, signal processing, fault 

detection, and diagnostics

Illustration Source: [9]
 NATIONAL RENEWABLE ENERGY LABORATORY

Performance Monitoring Using SCADA Data

12

SCADA: Supervisory Control and Data Acquisition Illustration Source: [5]
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Performance Monitoring

13

 Classification of Measured Parameters [11]: 

• Wind parameters: e.g., speed, deviation 

• Performance parameters: e.g., power output, rotor speed, blade 

pitch angle 

• Vibration parameters: e.g., tower acceleration, drivetrain 

acceleration 

• Temperature parameters: e.g., bearing and gearbox temperature. 

 Grouping of Control System Status Report [12]:

• Status codes: e.g., error, warning

• Operating states: e.g., brake, start, yaw, pitch. 

 Analysis: 

• Correlate different groups of parameters (e.g., power and wind), 

develop models for normal operational states, and use these 

models to identify abnormal scenarios 

• Conduct statistical analysis of events (e.g., status codes) 

experienced by turbines at a wind plant.
 NATIONAL RENEWABLE ENERGY LABORATORY

Performance Monitoring [5, 14] 

14

 Benefits: 

• Readily available and no need of investment in dedicated 

condition monitoring instrumentation

• Helpful for identifying outliers that may need further 

inspection by looking at key performance parameters or 

status codes. 

 Drawbacks: 

• May not be straightforward in pinpointing exact damaged 

subsystems/components (e.g., bearings or gears inside 

gearboxes) 

• Many false alarms caused by varying loads experienced by 

turbines 

• Does not meet full turbine condition monitoring needs, such 

as fault diagnosis. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Example: Main Shaft Bearing [15]

15

Main Shaft Bearing 

Fault
 NATIONAL RENEWABLE ENERGY LABORATORY

Condition Monitoring [5, 13]

16

 Typical Techniques: 

• Acoustic emission (e.g., 

stress wave) or 

vibration analysis 

• Oil. 

 Real-time continuous 

or offline periodic 

 One or a 

combination of a 

few. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Condition Monitoring with the Drivetrain as a Focus 

17

 Raw Signal Examples: 

• Accelerations, acoustic emissions 

• Oil debris counts, oil cleanliness measurements.

 Feature (Condition Indicator) Examples: 

• Preprocessing: filtering

• Time-domain statistical parameters: peak, root mean square

• Frequency domain: gear meshing frequencies and 

sidebands, bearing fault frequencies, and their statistical 

values. 

 Typical Diagnosis: 

• Trending or rate of changes of features or condition 

indicators 

• Appearance of frequency components corresponding to 

certain faults or abnormal modulation of signal spectra 

• Violating thresholds set for certain features. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Condition Monitoring 

18

 Benefits: 

• Capturing high-frequency dynamics normally not achievable 

with a typical SCADA system 

• Identifying more failure modes occurred to turbine 

subsystems or components 

• Pinpointing exact damaged locations/components 

• Enabling condition or reliability-based maintenance, 

prognostics, and health management.

 Drawbacks: 

• Additional investment required for instrumentation and 

monitoring service

• Dedicated resources on data analysis and interpreting 

results. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Case Study: A 750-Kilowatt Test Gearbox 

19

1. Completed dynamometer run-in test

2. Sent for field test: experienced two oil losses 

3. Stopped field test 

4. Retested using 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

Gearboxes: Vibration Analysis [16] 

20

 Intermediate-speed-shaft 

sensor

 Dynamometer test of the 

healthy test gearbox (left) 

indicated normal gearbox 

behavior.

 Dynamometer retest of the 

damaged gearbox (right) 

indicated abnormal behavior 

• More side band frequencies

• Elevated gear-meshing 

frequency amplitudes.

 Various condition 

indicators can be 

defined to ease the 

fault diagnostics 

process. 
 NATIONAL RENEWABLE ENERGY LABORATORY

Test Gearbox Lubrication Diagram [17] 

21

Wind Farm/Photo by Iberdrola Renewables, Inc., NREL 16707

Kidney Loop

Pump

0.65 GPM

Filter

3 μm Bypass 

Valve

40 psi

Sample 

Collection

Port

K5

ISO Cleanliness Level

K2 

Wear Debris

K3 

Wear Debris

Gearbox

P

P P

F

Filtered Working Fluid (10μ)

Inline Filter Loop

Filter

50 um 

Filter

10 um 

Flowmeter

Glycol/Oil 

Heat Exchanger (HX)

Relief Valve

200 psi

Pump

22 gpm

dP sensor dP sensor

P 

Sensor

Unfiltered 

Sump Oil 

Unfiltered Return Oil (Pressure Relief) Relief Return

Filtered (3μ, 0.24GPM) + Unfiltered (0.026GPM)

T

T

P

Relief Valve

25 psi

HX Pressure 

Bypass

K1

Inline Oil Debris

Gearbox 

Suction

Kidney Loop Return

Offline 

Suction

Gearbox Pressure

K4 

Oil Condition
22

Gearbox: Oil Debris Monitoring by K1 [17]

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.
23

Gearbox: Oil Debris Monitoring by K1~K3 [17]

0

100

200

300

400

500

600

700

800

900

1000

Date

0

50

100

150

200

250

300

Date

0

10

20

30

40

50

60

70

Date

 Main-loop and offline-loop 

sensors 

• Similar trends 

• Different absolute 

values: sensor 

mounting location 

impacts.
24

Gearbox: Oil Cleanliness by K4 [17]

0

5

10

15

20

25

13:3713:4313:4913:5514:0114:0714:1314:1914:2514:3114:3714:4314:4914:5515:0115:0715:1315:1915:2515:3115:3715:4315:4915:5516:0116:0716:1316:1916:2516:3116:3716:4316:4916:5517:0117:07

ISO 4406 Cleanliness Code 

Time

Oil Cleanliness Level 

ISO > 4 um ISO > 6 um ISO > 14 um

100% Rated 

Load

Generator 

Offline 

Main 

Lubrication 

Pump 

Started 

Generator 

Online 

Load 

Reduced to 

50% Rated 

Gearbox 

Stopped 

ISO: International Standards Organization
25

Gearbox: Oil Cleanliness by K4 (Cont.) [17]

1 2 3 4

Transitional Periods 

100% Rated 

Torque, 1800 rpm 

50% Rated 

Torque, 

1800 rpm 

50% 

Rated 

Torque, 

1800 rpm 

25% Rated 

Torque, 1200 rpm

Turbine Shut Down 

with Kidney Loop 

Filtration Running 
 NATIONAL RENEWABLE ENERGY LABORATORY

Gearbox: Oil Sample Analysis [17] 

26

 Results: dynamometer test of the reference gearbox

• Particle counts: important to identify particle types.

Reference Limits Analysis Results

• Element identification
27

Summary: Typical Practices

 Most performance monitoring data analysis techniques 

can identify abnormal behaviors and be used for initial 

screening of potential turbine issues.

• Readily available measured parameters and status codes 

• Limited when carrying out a full condition monitoring of wind 

turbine subsystems/components.

 Temperature may be used as a reliable indicator for the 

condition monitoring of main bearings, generator 

bearings, or gearbox high-speed stage bearings, but may 

not provide enough lead time to save the monitored 

component.

 Most condition monitoring data analysis techniques can 

help pinpoint specific subsystems/components with 

faults and enable prognostics and health management.

. 
28

Summary: Typical Practices (Cont.)

 Vibration analysis appears to be the most widely 

investigated and reported technique. It can monitor 

the health of most drivetrain, and even turbine, 

subsystems/components.

 May have challenges with low-speed-stage 

subsystems/components.

 Results from counting oil debris are easier to interpret 

and provide unique information on gearboxes 

(typically the only oil-lubricated subsystem in a wind 

turbine) because damaged gearboxes release particles 

at increased rates.

• Effective for monitoring gearbox component damage, and 

similar trends can be obtained between main and kidney 

filtration loops.

• Not effective for pinpointing damage locations. 
29

Summary: Typical Practices (Cont.)

 Measurements of oil cleanliness levels can be used 

to monitor and control the run-in of wind turbine 

gearboxes.

• Transient events appear more damaging. 

 When obtaining particle counts through oil-sample 

analysis, attention should be given to identifying 

particle types.

 Periodic oil-sample analysis may help pinpoint failed 

components and root-causes. 
30

Summary: Typical Practices (Cont.)

 Dedicated condition monitoring 

systems require additional 

investment for instrumentation 

and resources for data analysis or 

results interpretation.

 Given the diverse and complex 

failure modes seen in wind turbine 

drivetrains, an integration 

approach is recommended.

 Start with an initial digest of 

SCADA data and then fuse several 

dedicated techniques by 

considering their advantages and 

disadvantages. 

Scuffing

Spalling

Photo Credits: Andy Milburn, Milburn Engineering 

(top); and Ryan Evans, Timken (bottom)
31

R&D Activities 

 Efforts not widely practiced by the wind industry. 

 All layers within the following prognostics and health 

management architecture 

• Sensing: L1 

• Signal Processing and Modeling: L2-L5 

• O&M: L6-L7.

Illustration Source: [9]
32

Sensing [14, 18]

 Strain measurements: external 

nondestructive 

 Ultrasonic: nondestructive evaluation 

 Shock-pulse method: low-speed components 

 Filter-element analysis: complementary to 

oil-sample analysis 

 Electric-signature analysis: only verified at 

test rig or small-scale wind turbines 

 Oil-condition measurements: oil property 

deterioration over age; long-term study is 

needed.

Photo by Jonathan Keller, NREL 27895
33

Signal Processing and Modeling 

 Time-Frequency Analysis [5]

• Wavelet transform and Wigner-Ville distribution: 

nonstationary not nonlinear 

• Empirical mode decomposition: nonstationary and 

nonlinear, desired frequency range identification not 

flexible. 

 Data-Driven Modeling [5, 19]

• Neural network: efficient but hard to train 

• Genetic programming: mathematically simulate complex 

problem but hard to understand physical meaning 

• Regression analysis based on normal conditions and 

discrepancy evaluations: SCADA data mining and modeling 

not meeting complete prognostics and health 

management requirements.

 Physics-Based Modeling [20]

• Bearing life model and miner’s rule: component-specific 

may become complex for a turbine.
34

O&M

 Not many activities for land-based wind plants [21, 22]

• Partially observed Markov decision process for optimal repair 

strategies of wind turbines to minimize cost under stochastic 

weather conditions

• Cost-benefit-risk model for evaluation of wait-to-maintain option 

with assumed remaining useful life and a prognostic indication.

 Active for offshore wind plants [23, 24]

• Time domain Monte-Carlo simulation to find out the most costeffective approach to allocate O&M resources considering 

environmental conditions, transportation systems, failures, and 

repairs

• Bayesian theory for optimal planning of inspections and 

maintenance based on a single wind turbine and a single 

component considering inspections, repairs, and loss of 

production.
35

R&D Activities 

 Sensing 

• Need to justify the added cost to be widely accepted by the industry 

• Has potential especially for those complementary to the popular 

vibration and oil analysis such as shock-pulse, filter-element, and 

electric-signature analysis. 

 Signal Processing and Modeling 

• Academically very attractive but oftentimes computationally expensive 

and hard to implement in the field 

• Modeling work faces validation challenges as it normally needs longterm data collection, which is very difficult within the wind industry.

 O&M

• Less attractive to owners and operators of land-based wind power 

plants, as they may face parts and technician availability challenges

• More attractive to offshore wind plant owners and operators because of 

the high value proposition mainly caused by even less accessibility and 

additional logistics and scheduling complexities.
36

Concluding Remarks

 Summary

 Future R&D Opportunities

Offshore wind turbine. Photo by Eric Nelson, NREL 21965
37

Summary

 Prognostics and health management in the wind industry has been 

focusing on the first four layers, and gaps with the other few layers 

need to be filled to maximize the benefits of prognostics and health 

management to wind. 

Illustration Source: [9]
38

Future R&D Opportunities 

 O&M 

• Fusion of various data streams to optimize O&M 

practices, reduce loads, and extend life of turbine 

subsystems/components 

• Automate data interpretation to deliver actionable 

maintenance recommendations.

 Signal Processing and Modeling 

• Research on improved use of SCADA data

• Improve accuracy and reliability of diagnostic 

decisions, including level of severity evaluation 

• Develop reliable and accurate prognostic 

techniques to enable remaining useful life 

estimation of turbine components/subsystems. 
39

Future R&D Opportunities

 Sensing

• Complementary or superior to solutions currently adopted 

by the wind industry 

• New technologies targeting next critical 

subsystem/component in wind turbines 

 Uncertainty representation and interpretation, 

quantification, propagation, and management [25]

 Perspective shift from individual turbine to entire plant

• Research plant-level or fleet-wide condition monitoring and 

asset management technologies 

• Big data analytics and Internet of Things 

 Field application feasibility study and cost effectiveness 

justification for any prognostics and health management 

solutions to impact the industry in a big way. 

Challenging, yet rewarding.
40

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43

Thanks for Your Attention!

This work was funded by the 

U.S. Department of Energy 

under Contract No. DE-AC36-

08GO28308 with the National 

Renewable Energy Laboratory. 

Funding for the work was 

provided by the DOE Office of 

Energy Efficiency and 

Renewable Energy, Wind and 

Water Power Technologies 

Office. The author is solely 

responsible for any omissions 

or errors contained herein. 

Photo from HC Sorensen, Middelgrunden Wind Turbine Cooperative, NREL 17855 

shuangwen.sheng@nrel.gov

303-384-7106

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

partners working with NREL!