NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Wind Turbine Drivetrain 

Condition Monitoring During 

GRC Phase 1 and Phase 2 

Testing 

S. Sheng, H. Link, W. LaCava, J. van Dam, 

B. McNiff, P. Veers, and J. Keller

National Renewable Energy Laboratory

S. Butterfield and F. Oyague

Boulder Wind Power

Technical Report

NREL/TP-5000-52748

October 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Wind Turbine Drivetrain

Condition Monitoring During 

GRC Phase 1 and Phase 2 

Testing 

S. Sheng, H. Link, W. LaCava, J. van Dam, B. 

McNiff, P. Veers, and J. Keller

National Renewable Energy Laboratory

S. Butterfield and F. Oyague

Boulder Wind Power

Prepared under Task No. WE11.0305

Technical Report

NREL/TP-5000-52748

October 2011
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iii

Acknowledgements 

The National Renewable Energy Laboratory’s contributions to this report were funded by the 

Wind and Water Power Program, Office of Energy Efficiency and Renewable Energy of the U.S. 

Department of Energy, under contract No. DE-AC02-05CH11231. The authors are solely 

responsible for any omission or errors contained herein. NREL wishes to acknowledge and thank 

the Office of Energy Efficiency and Renewable Energy and its staff who have supported this 

work from its inception. Specifically, NREL would like to thank Mark Higgins and Michael 

Derby for their support and guidance. NREL also appreciates the support from the drivetrain 

condition monitoring research partners. 
iv

Abstract 

Wind turbines have historically had reliability issues, which subsequently increase the overall 

cost of energy. The majority of these issues are caused by faults in the drivetrain, led by the main 

gearbox. These issues are widespread, existing across all turbine sizes and manufacturers. One 

means to mitigate the detrimental effect of reliability issues is through condition monitoring. 

Condition monitoring is a method to assess a system’s health; enabling proactive maintenance 

planning, reducing downtime, reducing operations and maintenance costs and, to some extent, 

increasing safety. In this report, vibration, acoustic emission (specifically stress wave), electrical 

signature, oil cleanliness, oil debris, and oil sample analysis condition monitoring techniques 

were investigated for two identical 750 kW wind turbine gearboxes, in both a dynamometer test 

cell and field installation. The two test gearboxes are referred to as Gearboxes 1 and 2 in this 

report. The strengths and weaknesses of the different techniques were assessed. The feasibility of 

using oil cleanliness monitoring to determine the length of the run-in procedure was investigated

on both gearboxes. Both demonstrated that, to make the run-in process sufficient, longer run-in 

durations at each torque level may be needed as compared to the current standard run-in 

procedure of prescribed durations at each torque level. Without fully completing the run-in, 

surface roughness remains excessive leading to increased contact stresses when the gearbox is 

placed into service and potentially leading to premature failures. Gearbox 1 was installed in a 

turbine at the Ponnequin Wind Farm and, after 300 hours of operation, it experienced two oil loss 

events and excessive temperatures that caused damage to some of its internal components. The 

gearbox was subsequently removed and inspected. Since the damage to the teeth was not severe, 

gearbox 1 also was installed and retested in the National Wind Technology Center's (NWTC)

dynamometer before it was disassembled. Gearbox 2 was tested only in the dynamometer and 

was undamaged. The results were compared between gearboxes for each monitoring technique 

and the technique itself was evaluated for its detection capability. Vibration, acoustic emission, 

and oil debris monitoring all demonstrated the capability of distinguishing between the healthy 

and damaged gearbox components. It was possible to identify which stage of the gearbox was 

damaged, but not exactly which component was damaged for some gears and bearings inside the 

gearbox. Electrical signature analysis did not show any indication of the gear teeth damage, most 

likely because the damage to the teeth was not severe. To detect dominant failure modes in a 

gearbox, a combination of vibration or acoustic emission and oil debris monitoring techniques is 

recommended. Each technique is sensitive to sensor location and even orientation, and 

maintenance alerts are specific to each component and damaged part.
v 

Table of Contents

Acknowledgements...................................................................................................... iii

Abstract......................................................................................................................... iv

Table of Contents.......................................................................................................... v

List of Tables ............................................................................................................... vii

Acronym List .............................................................................................................. viii

Introduction ................................................................................................................... 1

The Gearbox Reliability Collaborative (GRC).............................................................. 3

Condition Monitoring (CM) .......................................................................................... 3

GRC CM Partnerships and Documents ...................................................................... 4

GRC CM Phase 1 and Phase 2 .................................................................................. 4

Drivetrain CM Approach and Rationale....................................................................... 5

CM Systems Investigated............................................................................................. 8

SKF WindCon ............................................................................................................. 8

SwanTech SwanWind................................................................................................. 8

Kittiwake Online Sensor Suite and Particle Content Sensor ....................................... 8

Macom TechAlert 10................................................................................................... 9

GasTOPS MetalScan 3000......................................................................................... 9

Hydac CSM 1220........................................................................................................ 9

SKF Baker EXP 4000 ............................................................................................... 10

Eaton Motor CM System........................................................................................... 10

NREL Customized Vibration-based CM System....................................................... 10

Tests and CM System Configurations....................................................................... 12

Test Articles .............................................................................................................. 12

Tests Conducted....................................................................................................... 16

CM System Configurations ....................................................................................... 19

Test Results and Discussions ................................................................................... 25

Vibration Analysis ..................................................................................................... 25

Stress Wave Analysis ............................................................................................... 28

Oil Monitoring............................................................................................................ 29

Oil Cleanliness Level Monitoring............................................................................... 30

Oil Debris Monitoring ................................................................................................ 31

Oil Condition Monitoring............................................................................................ 35

Oil Sample Analysis .................................................................................................. 36

Electrical Signature Analysis..................................................................................... 37

Summary of Results.................................................................................................. 37

Challenges in Wind Turbine Drivetrain CM and Future R&D Areas........................ 39

Challenges to Implementation of Wind Turbine Drivetrain CM ................................. 39

Future R&D areas in Wind Turbine Drivetrain CM .................................................... 41

Conclusions................................................................................................................. 42

References................................................................................................................... 43

Appendix A – Project Partners................................................................................... 46

Appendix B – Other Activities.................................................................................... 47

Workshop.................................................................................................................. 47

Round Robin Project................................................................................................. 47

Appendix C – Publications and Presentations ......................................................... 48
vi 

List of Figures

Figure 1. Downtime caused by turbine subsystems ........................................................................ 1

Figure 2. Annual failure frequency of turbine subsystems ............................................................. 2

Figure 3. Typical utility-scale wind turbine drivetrain ................................................................. 12

Figure 4. Exploded view of the GRC test gearbox ....................................................................... 13

Figure 5. GRC gearbox layout and bearing nomenclatureTest Platforms .................................... 13

Figure 6. Diagram of NREL 2.5-MW dynamometer test facility ................................................. 14

Figure 7. NREL dynamometer test stand with the test article installed. PIX #16913. ................ 15

Figure 8. GRC test turbine at Xcel Energy's Ponnequin wind farm. PIX #19257. ...................... 16

Figure 9. CM system configuration in Phase 2 Gearbox 1 Dynamometer Retest ....................... 20

Figure 10. NREL customized vibration-based CM system sensor locations ................................ 21

Figure 11. Accelerometer installation on NREL's customized vibration-based CM system ........ 22

Figure 12. Lubrication system schematics for Phase 2 Gearbox 1 Dynamometer Retest ............ 23

Figure 13. Gearbox 1 high speed stage (taken in field after second oil loss event). PIX #19600. 25

Figure 14. Gearbox 1 high speed stage gear damage (during Gearbox 1 disassembly). PIX 

#19599........................................................................................................................................... 25

Figure 15. Results from a vibration-based CM system (healthy gearbox) ................................... 27

Figure 16. Results from a vibration-based CM system (damaged gearbox) ................................. 27

Figure 17. Stress Wave Amplitude Histogram (healthy gearbox) ................................................ 28

Figure 18. Stress Wave Amplitude Histogram (damaged gearbox) ............................................. 29

Figure 19. Oil cleanliness level (run-in) ....................................................................................... 31

Figure 20. Cumulative particle counts obtained by sensor K1 (run-in) ........................................ 32

Figure 21. Cumulative particle counts obtained by sensor K2 (run-in) ........................................ 32

Figure 22. Cumulative particle counts obtained by sensor K3 (run-in) ........................................ 33

Figure 23. Cumulative particle counts obtained by sensor K1 (damaged gearbox) ..................... 33

Figure 24. Ferrous particle counts by sensor K2 categorized into five size bins (run-in) ............ 34

Figure 25. Ferrous particle counts by sensor K3 categorized into five size bins (run-in) ............ 34

Figure 26. Oil condition sensor readings (field test)..................................................................... 35

Figure 27. Oil condition sensor readings (dynamometer retest) ................................................... 36

Figure 28. Oil sample analysis (run-in) ........................................................................................ 37
vii 

List of Tables

Table 1. Advantages and limitations of lubricant and vibration CM techniques ............................ 5

Table 2. GRC CM-related testing ................................................................................................. 17

Table 3. CM system implementation at different stages of the GRC tests ................................... 19

Table 4. Sensor notations and descriptions ................................................................................... 21
viii

Acronym List

Acronym Definition

AE acoustic emission

CBM condition-based maintenance

CM condition monitoring 

COE cost of energy 

CRB cylindrical roller bearing

DAS data acquisition system

DOE Department Of Energy 

fcCRB full complement cylindrical roller bearing

FFT Fast Fourier Transform

GRC Gearbox Reliability Collaborative 

GMF gear meshing frequency 

HS high speed 

HSS high-speed shaft

ISO International Organization for Standardization

ISS intermediate-speed shaft

LSS low-speed shaft

NREL National Renewable Energy Laboratory 

O&M operation and maintenance

PLC planet carrier 

R&D research and development 

RPM revolutions per minute

SCADA supervisory control and data acquisition

SEE spectral emitted energy 

SEM scanning electron microscope 

SRB spherical roller bearing

TRB tapered roller bearing 
1 

Introduction

Wind energy is currently the fastest growing energy source among various renewable energy 

options in the world [1]. By the end of 2010, the global cumulative installed wind power had 

reached more than 197 gigawatts (GW) [2]. However, the industry still experiences premature 

turbine component failures. A summary of the downtime of turbine components is plotted in

Figure 1. The data represent about 27,000 turbines, ranging from 500 kW to 5 MW, for both 

onshore and offshore applications [3]. Three elements that comprise the wind turbine drivetrain - 

the gearbox, generator, and main shaft/bearing – together cause the majority of the total turbine 

downtime. The gearbox is the leading contributor to total turbine downtime. 

Figure 1. Downtime caused by turbine subsystems
2 

Figure 2 illustrates the annual failure frequency of turbine subsystems based on the 2009 Wind 

Stats Newsletter data. The top three most frequently failed subsystems are electric systems, the 

gearbox, and the generator. The highest rate, at approximately 27%, is equivalent to about 0.6 

failures for each turbine subsystem annually, based on data reported by Reliawind [4]. 

Figure 2. Annual failure frequency of turbine subsystems

These premature turbine subsystem failures lead to increases in operation and maintenance 

(O&M) costs, and subsequently, the cost of energy (COE) for wind power. As more turbines are 

installed offshore and as turbines increase in size, these failures will become even more costly. 

To make wind power more competitive, there is a need for the industry to reduce turbine 

downtime and improve reliability. 

When crane cost is taken into consideration, maintenance events for the gearbox, generator, 

rotor, and main shaft/bearing are the most costly. Among the aforementioned subsystems, the 

gearbox stands out as causing the most downtime as shown in Figure 1, failing second most 

frequently, as shown in Figure 2, and costing the most to maintain throughout a turbine’s 20 

years of design life [5]. Therefore, if only one subsystem can be targeted for a reliability 
3 

improvement, the gearbox is the obvious choice. Other subsystems are typically easier and 

cheaper to fix, although they may fail more frequently. 

The Gearbox Reliability Collaborative (GRC)

Improving reliability of wind turbine gearboxes has been challenging for the industry. Despite 

reasonable adherence to accepted gearbox design standards and practices, wind turbine 

gearboxes sometimes do not achieve their design life goals of 20 years [6]. The problem of 

premature wind turbine gearbox failures is widespread; it affects most original equipment 

manufacturers, and exists in spite of improved gearbox manufacturing processes [7]. 

Institutional barriers among entities in the wind turbine supply chain hinder communication and 

feedback during the design, and operation and maintenance of wind turbine gearboxes, making it 

difficult for a single entity to find the best solutions to the gearbox reliability problem [6].

Collaborative efforts involving all entities from throughout the gearbox supply chain can work 

together to identify shortcomings and offer recommended improvements for wind turbine 

gearboxes. 

To meet this need, the National Renewable Energy Laboratory-led Gearbox Reliability 

Collaborative (GRC) project was established. Simply put, the GRC is a consortium that engages

key representatives in the wind turbine gearbox supply chain, including turbine owners, 

operators, gearbox manufacturers, bearing manufacturers, lubrication companies, and wind 

turbine manufacturers. Its goal is to improve gearbox reliability and increase turbine uptime. To 

achieve these, the GRC takes a multi-track approach [8], which includes modeling and analysis, 

dynamometer testing, field testing, condition monitoring, and developing a gearbox failure 

database [6]. This report focuses on condition monitoring, which will be discussed in detail.

Condition Monitoring (CM)

Condition Monitoring (CM) is defined as the process of monitoring a parameter of condition in 

the machinery, such that a significant change is indicative of a developing failure [9].The 

benefits of CM include [10]: 1) detecting incipient failures early, thereby reducing the chances of 

catastrophic failures; 2) accurately evaluating component health conditions, which has the 

potential to enable more cost-effective O&M; and 3) analyzing root causes, which may provide 

the inputs for improved turbine operation, control strategy, and component design. Although the 

economics of deploying CM for a wind park are case dependent [11], some studies have shown 

the estimated return on assumed cost being better than 10:1 [12], with total return on investment 

achieved in less than three years [13]. These benefits will be even more dramatic if turbines are 

installed offshore where accessibility is a huge challenge. In a broad sense, CM of an onshore 

utility-scale wind turbine can target almost all of its major subsystems, including the blades, 

nacelle, drivetrain, tower, and foundation. However, the CM discussed in this report is focused 

solely on the wind turbine drivetrain. 

There are many reasons to conduct drivetrain CM research within the GRC. Today, as when the 

GRC was started in 2007, wind plant owner/operators primarily practice reactive or time 

interval-based maintenance. A paradigm shift to condition-based maintenance (CBM), enabled 

by various CM techniques, can help wind plant owner/operators reduce their O&M cost, which is 

an important piece in the overall energy cost for wind power. In addition, CM can capture the 

condition of individual turbines and supplement improved gearbox design practices. Lastly, the 

GRC's dynamometer and field tests provide the opportunity to investigate the strengths and 
4 

limitations of different CM techniques and recommend CM practices to the industry. Because it

is funded by the U.S. Department of Energy, the GRC has the unique capability to establish 

public domain CM datasets 

GRC CM Partnerships and Documents

Since the inception of this research, the number of GRC CM partners has grown to 27 

organizations. Appendix A lists all of the members who wish to be acknowledged in this report. 

Eleven of these 27 partners contributed to the work reported here, and they typically loaned CM 

equipment or provided CM services for this research. Various CM equipment provided by the 

partners represents a range of CM techniques. The 16 partners listed in Appendix B are involved 

in the wind turbine gearbox condition monitoring round robin project, which will be discussed in 

a report to be issued in the spring of 2012. Three partners, C.C. Jensen, Lubrizol, and Castrol, 

will not be mentioned in the latter stage of this report as they are not CM equipment suppliers or 

service providers. C.C. Jensen contributed some lubricant conditioning systems, while Lubrizol 

and Castrol supported the research with lubrication oil and technical advice on lubricant testing. 

Findings obtained through the CM research have been reported at various papers at conferences, 

in presentations at workshops, and in NREL technical reports. As of September 2011, about 15 

presentations, papers, or reports have been published and are listed in Appendix C. 

GRC CM Phase 1 and Phase 2

This report is a summary of Phases 1 and 2 of the GRC CM testing. It begins with a discussion 

of the GRC CM approach and rationale, and is followed by a discussion of the various CM 

technologies investigated. The subsequent section introduces the test articles, test platforms, tests 

conducted, and CM system configurations. Test results and observations are presented, with a 

focus on the CM of test gearboxes. The next section touches on recommended practices for 

conducting wind turbine drivetrain CM. Finally, the report concludes with a discussion of 

challenges facing wind turbine drivetrain CM and potential areas for future Research and 

Development (R&D) work. 
5 

Drivetrain CM Approach and Rationale 

A wind turbine is a complex system with various subsystems that can fail in dramatically 

different modes. Accordingly, drivetrain CM takes an integrated approach because no single 

technique can provide a comprehensive and reliable solution to cover all possible wind turbine 

failure modes. For example, lubricant CM (such as oil debris particle counts) will not detect a 

machine imbalance, misalignment, shaft cracks, and resonances that are detectable by vibration 

CM. Conversely, vibration CM will not detect water or particles in the lubricant offered by 

lubricant CM. Several advantages and limitations of lubricant CM and vibration CM are listed in 

Table 1 [14].

Table 1. Advantages and limitations of lubricant and vibration CM techniques 

Condition Lubricant CM Vibration CM Correlation

Machine Unbalance Not Applicable Strength

Vibration program can 

detect an unbalance 

condition. Lube analysis 

will eventually see the 

effect of increased bearing 

load.

Water in Oil Strength Not Applicable

Water can lead to a rapid 

failure. It is unlikely that a 

random vibration scan 

would detect the anomaly.

Gear Wear Strength Strength

Vibration techniques can 

predict which gear. Lube 

analysis can predict the 

type of failure mode.

Alignment Not Applicable Strength

Vibration program can 

detect a misalignment 

condition. Lube analysis 

will eventually see the 

effect of increased / 

improper bearing load.

Lubricant Condition Strength Not Applicable The lubricant can be a 

significant cause of failure.

Resonance Not Applicable Strength

Vibration program can 

detect a resonance 

condition. Lube analysis 

will eventually see the 

effect.

When GRC testing began, four CM techniques were initially applied: 

1. Acoustic emission (AE), specifically stress wave

2. Vibration

3. Offline filter loop (or kidney loop) oil debris and condition monitoring 

4. Offsite oil sample analysis. 
6 

The offline filter loop oil condition monitoring is specifically referred to the measurement of oil 

total ferrous debris, oil quality and oil moisture. As the GRC tests progressed, inline filter loop 

(or main loop) oil debris monitoring and electrical signature-based techniques were added. 

By combining AE and vibration CM, the measurable dynamic frequency range can be extended 

to above 20 kHz. Vibration can typically be measured in terms of displacement by proximity 

probes, or in terms of acceleration, by accelerometers. The proximity probe was not considered, 

as it is typically used for shaft displacement measurements. The wind turbine main shaft is not a 

commonly failed component in the drivetrain and is not included in this research. In addition, AE 

or stress wave monitoring has the potential to detect abnormal lubricant film thickness [15]. The 

main considerations in adding oil debris and condition monitoring, and offsite oil sample 

analysis techniques is to combine the early active machine wear detection capability of oil 

monitoring techniques with AE and vibration CM’s crack location ability [16]. The combination 

of the three techniques may provide the earliest possible detection of component failure or 

structural deterioration. 

There were two options to set up the CM systems for the three adopted techniques: 1) have 

NREL customize the needed sensors, data acquisition systems (DAS), and signal processing 

algorithms; or 2) choose a commercial package under each CM technique and have NREL do a 

minimal amount of DAS customization and signal processing algorithm development. The 

second option was chosen because it required less time to implement and allowed the GRC to

meet its planned gearbox test schedule. 

There are several commercial packages available for each CM technique. To decide which 

package to use for this CM project, the GRC used the following criteria: 1) use only one CM 

system for each technique. (There is no plan to evaluate different CM packages under the GRC.); 

2) choose the best possible package (in terms of performance) among interested CM system 

suppliers; and 3) research previous wind industry experience with each particular package. 

Based on these criteria and through study and communication with interested CM package 

suppliers, the systems selected to start the drivetrain CM project include: SKF WindCon for 

vibration; Swantech SWANwind for AE; and Kittiwake Online Sensor Suite, Macom TechAlert

10, and Hydac CSM 1220 for oil. (The reasons for this are explained below.) All but the Hydac 

package, which was bought by NREL as laboratory test equipment, are on loan to NREL for 

conducting drivetrain CM research. For oil sample analysis, Herguth Laboratories Inc. agreed to 

become a project partner and support the CM research. 

Although the selection of multiple lubricant CM packages appears contradictory to the criterion, 

each package provides a different aspect of information on the monitored lubricant and their 

results do not overlap. Specifically, the Kittiwake Online Sensor Suite is a compact unit capable 

of measuring three lubricant parameters: total ferrous debris in ppm, relative humidity in 

percentage, and oil quality (changes in the level of contaminants such as soot, oxidation 

products, glycol, and water) on a customized scale. The outputs provided by this suite will be 

referred to as offline oil condition monitoring results in this report. The Macom TechAlert 10 

counts oil debris, both ferrous and non-ferrous, particles and divides each particle type into five 

bins. The minimum detectable ferrous particle size is 50 μm and non-ferrous particle size is 150 

μm. The Hydac CSM 1220 was purchased by NREL as a laboratory unit to measure the lubricant 

cleanliness level according to ISO 4406:1999 [17]. This level basically reflects the amount of 
7 

particles in the following three size bins: >4 μm, >6 μm, and >14 μm for 1 ml of monitored 

lubricant. Making sure the lubricant is clean to the ISO standard before it is put into a gearbox is 

very critical for achieving the turbine’s expected performance and extending its service life. 

As the GRC tests progressed, inline (or main loop) oil debris monitoring was added. The reason 

was to compare its performance with the offline oil debris monitoring technique. In the context 

of this report, the inline filter loop refers to part of the lubrication system that has a full flow of 

lubrication oil. It operates when the turbine is operating and provides an ample oil supply to 

many or all bearings and gears in the gearbox. The inline filter loop also provides filtering 

suitable for removal of moderate to large particles (>10 μm) during turbine operation when most 

of these particles are generated. It is frequently used for oil cooling. On the other hand, the 

offline filter loop refers to part of the lubrication system that has a small portion of the full oil 

flow. The offline filter loop operates continuously. It provides filtering of small particles (>3 

μm). By following the criteria established at the beginning of the GRC tests, the inline lubricant 

CM was implemented using MetalScan 3000 series oil debris monitoring sensors provided by 

GasTOPS, Inc.

Electrical signature-based techniques also were added as the GRC tests progressed. 

Considerations for studying electrical signature-based techniques include: 1) whether these 

measurements can detect damage in the gearbox or in the main bearing along the drivetrain in

geared wind turbines; and 2) whether these measurements are effective in assessing the health of 

the generator in direct drive wind turbines. However, electrical signature-based CM partners

were difficult to find due to the dominant use of vibration CM, AE, and lubricant CM by the 

wind industry since the monitoring of motors are the primary targets. In the end, the electrical

signature-based CM systems chosen were the Motor Dynamic Analyzer platform provided by 

SKF Baker Instrument and a motor CM system provided by Eaton. The reason to investigate two 

systems is that the SKF motor dynamic analyzer has been commercialized and has a lot of 

experience in motor testing, though not much experience in generators. The Eaton system is a 

prototype designed to identify potential gearbox and generator damage. However, neither of 

these two is well established for wind turbine drivetrain CM. It is clear that electrical signaturebased CM techniques are not as widely adopted by the wind industry as other monitoring 

techniques. 

The integrated approach adopted in this research is in line with typical industry practices [14], 

that is, it combines vibration or stress wave monitoring with lubricant CM. It also adds other 

monitoring techniques. Although the integration of these additional techniques may not gain 

popularity in commercial applications due to increased costs, it allows the GRC to investigate 

different CM techniques that are potentially beneficial to future wind industry field applications. 
8 

CM Systems Investigated 

Based on the different types of CM techniques and systems chosen in the previous section, a 

detailed description of these CM systems is presented in the next section. 

SKF WindCon

The SKF WindCon [18] package is one of the vibration-based CM techniques. It typically has 

eight accelerometers of different sensitivities mounted onto the main components of the turbine 

drivetrain. It has a data acquisition unit in the turbine nacelle. For the dynamometer test, the data 

acquisition unit is located in the NREL Dynamometer Test Facility high bay. The SKF package 

also includes a software platform hosted on a local (in the tower base of the turbine or the 

dynamometer test facility control room) or remote computer (the SKF data server is in Sweden). 

The data analysis and view analysis results occur within the software, which streams raw and 

processed data to a database. 

The main function of the SKF package is to monitor health conditions of the main drivetrain 

components (i.e., main bearing, gearbox, and generator) based on measured acceleration data. 

For the GRC tests, one IMx-W (the data acquisition unit) and eight accelerometers are used in 

the dynamometer test of the first gearbox. These will later be moved to the field test. One IMx-S 

and another eight accelerometers are used in the other dynamometer tests. The IMx-S is an 

earlier version of the IMx-W. Functionally, these two units are similar. The use of two different 

packages was determined by their availability from SKF at the time each test was conducted. All 

of the tests were monitored through the SKF @ptitude Observer software package. 

SwanTech SwanWind

The SwanTech SWANwind [19] package represents an AE-based CM technique. Similar to the 

SKF CM system, the SwanTech package typically includes five SwanSensors mounted onto the 

main components of the turbine drivetrain, one SwanGuard located in the nacelle, (or, in the 

NREL Dynamometer Test Facility high bay), and one SwanServer located in the tower base, or 

in the test facility control room. 

The main function of the SwanTech package is to monitor the health condition of the main 

drivetrain components, i.e., the main bearing, gearbox, and generator, based on measured stress 

wave data. The GRC dynamometer and field tests each had two SwanTech packages. The reason 

two packages were used was to evaluate whether data collected in a sequential or synchronized 

manner made a difference in the drivetrain condition monitoring results. The sequential package 

had five SwanSensors mounted onto the main drivetrain components, and another three 

SwanSensors mounted onto the dynamometer gearbox and motor. The purpose of mounting 

sensors on the dynamometer was to study the relationships and dynamic responses between the 

dynamometer and the observed SwanSensor readings from each of the main drivetrain 

components. 

Kittiwake Online Sensor Suite and Particle Content Sensor 

The Kittiwake package was one tool used in the lubricant CM solution. It includes one sensor 

suite [20], which is composed of three types of sensors. The sensor suite is capable of monitoring 

the lubricant condition in terms of total ferrous debris in parts per million, relative humidity as a 

percentage, and oil quality (changes with the level of such contaminants as soot, oxidation 
9 

products, glycol, and water) on a customized scale. As mentioned earlier, its outputs will be 

referred to as offline oil condition monitoring results in this report. Another unit from Kittiwake 

was the particle content sensor [21], which was used to count oil debris, both ferrous and 

nonferrous, particles and divide each particle type into five bins. The minimum detectable 

ferrous particle size is 40 μm and the minimum nonferrous particle size is 135 μm. The data 

collected by the sensor suite and particle content sensor are wirelessly transmitted through a 

cellular modem to a server located in the United Kingdom and can be viewed through a web 

browser. 

For the GRC tests, one sensor suite was used in the dynamometer test of the first gearbox and 

later transmitted to the field test. The other sensor suite and particle content sensor were used in 

the other dynamometer tests. The main function of the Kittiwake sensor suite was to monitor the 

change in lubricant condition in terms of total ferrous debris, relative humidity level, and oil 

quality. The particle content sensor was added in the dynamometer tests to investigate the 

influences of the sensor mounting location on oil debris counting results. 

Macom TechAlert 10

The Macom package was another tool used for the lubricant CM solution. It is composed of one 

TechAlert 10 [22] particle counting sensor and one software package for recording and storing 

the collected data. Similar to the Kittiwake particle content sensor, the Macom TechAlert 10 

count oil debris, both ferrous and nonferrous particles, and divides each type of particle into five 

bins. The minimum detectable ferrous particle size is 50 μm and the minimum nonferrous 

particle size is 150 μm. The TechAlert 10 sensor also can be integrated with the SKF WindCon 

or SwanTech SWANWind package. 

The main function of the TechAlert 10 was to monitor oil debris particle counts. For the GRC 

tests, one TechAlert 10 was used in the dynamometer test of the first gearbox, which was later 

moved for use in the field test. It was the only oil debris monitoring package tested on the first 

gearbox. Another TechAlert 10 was used in the other dynamometer tests. 

GasTOPS MetalScan 3000

The GasTOPS system was another tool tested for the lubricant CM solution. It is composed of 

one MetalScan 3000 [23] oil debris particle-counting sensor and an Excel spreadsheet for 

recording and storing the collected data. Different from the Kittiwake particle content sensor and 

the Macom TechAlert 10, which are usually placed in a kidney loop on the lubrication system, 

the MetalScan sensor is placed in the main lubrication loop. There is a much higher flow rate in 

the main loop than in the kidney loop. The MetalScan monitors the oil debris particle generation 

in the main lubrication loop and allows the study of various influences from both inline and 

offline filter loops on oil debris particle counting results. For the GRC tests, one MetalScan 3000 

sensor was used in the dynamometer test of the second gearbox and later dynamometer tests. It 

was not used in tests on the first gearbox since there was no plan to investigate differences that 

might be caused by the offline and inline filter loops on the first gearbox.

Hydac CSM 1220

The Hydac CSM 1220 [24] is another tool used in the lubricant CM solution. NREL purchased it 

as a laboratory unit to measure lubricant cleanliness levels according to ISO 4406:1999. Its 

readings basically reflect the amount of particles in three bins: >4 micrometers (μm), >6 μm, and 
10 

>14 μm in 1-milliliter of monitored lubricant. Its purpose is to first ensure that the lubricant is 

clean before it is put into the gearbox and then, to evaluate whether the ISO cleanliness level is 

useful for controlling and monitoring wind turbine gearbox run-in. 

SKF Baker EXP 4000 

The SKF Baker Dynamic Motor Analyzer EXP 4000 [25] represents a commercialized electronic 

signature-based motor performance monitoring system. It measures three-phase current and 

voltage from the monitored motor. By feeding these signals to a portable data acquisition unit, 

with an integrated laptop, the package is capable of identifying degrading motor performance 

and overheating in motor operation, examining overall motor power conditions and motor 

performance, and monitoring the load, rotor cage condition, and torque ripples. The package also 

includes software hosted on an integrated laptop that is used to conduct data recording and 

analysis, view analysis results, and to export data. 

For the dynamometer test, the SKF Dynamic Analyzer was located in the NREL Dynamometer 

Test Facility high bay. For the field test, it was located at the turbine tower base. The entire 

package includes one EXP4000, including the portable data acquisition unit and its integrated 

laptop, one EP1000, including the interface box between the current transducers and the 

analyzer, and three current transducers. 

Eaton Motor CM System 

The Eaton Motor Condition Monitoring System represents a prototype effort for electronic 

signature-based motor health monitoring systems. It measures three-phase current and voltage 

from the monitored motor. The three-phase current is measured using three current transducers 

and the three-phase voltage is measured using a custom built data acquisition unit. The system 

digitizes the voltage and current information and transmits the data via Ethernet to a host laptop 

computer for storage and remote access purposes. The three-phase voltage and current data can 

be analyzed to determine the effectiveness of extracting gearbox failure signatures from the 

generator's electrical information. The data also will be processed to evaluate steady-state and 

transient power quality, drivetrain misalignment, and unbalance conditions, as well as potential 

generator issues, such as stator, rotor, and bearing degradation. 

For the dynamometer test, the Eaton Motor Condition Monitoring System was located in the 

NREL Dynamometer Test Facility high bay. The entire package included one data acquisition 

box, one laptop, and three LEM LT 2005-S current transducers. 

NREL Customized Vibration-based CM System 

As the GRC project progressed, it became clear that in order to release some CM test data before 

the GRC projects ends, a CM system was needed in the public domain. As a result, a vibrationbased CM system was customized by NREL. It is composed of twelve accelerometers. To 

simplify the customization task, data was collected at 40 kHz per channel using a National 

Instruments PXI -4472B high speed DAS. Low speed shaft (LSS) torque and generator speed 

were recorded, in addition to the accelerometer data. It is this system that made the wind turbine 

gearbox condition monitoring round robin project possible (see Appendix B). 
11 

For the dynamometer test, the NREL CM System was located in the NREL Dynamometer Test 

Facility high bay. The DAS software interface, hosted on a desktop computer, was located in the 

NREL Dynamometer Test Facility control room. 
12 

Tests and CM System Configurations 

The aforementioned CM systems were evaluated in various GRC tests. This section provides a 

brief description of the test gearboxes, test platforms, tests conducted, and CM system 

configurations. 

Test Articles 

To make the GRC research more valuable to the industry, a 750-kW rated gearbox was selected 

and redesigned to be representative of MW-sized fleet turbines. A 750-kW rating was chosen, 

because it is large enough to represent common wind turbines currently in use, yet small enough 

that it would be reasonably inexpensive to procure, modify, and test in the NREL 2.5 MW 

dynamometer. Two procured test gearboxes were identical aside from manufacturing variance. 

More details on the test gearboxes can be found in [6], and briefly described below. 

The drivetrain of a typical utility-scale wind turbine is illustrated in Figure 3. The test gearbox 

was installed in a 3-point suspension drivetrain configuration, which is a typical configuration 

for MW class turbines. One suspension point is at the main bearing and the other two points are 

on the gearbox torque arm supports. 

Hub Main shaft Gearbox Generator

Main bearing Generator shaft Bed plate

Brake

Figure 3. Typical utility-scale wind turbine drivetrain

The test gearbox uses three stages to obtain an overall gear ratio of 1:81.491. As shown in Figure 

4 , it is composed of one low-speed planetary stage and two parallel shaft stages. The planetary 

stage accommodates three planet gears. The annulus gear of this stage also serves as part of the

gearbox housing. The sun gear is set in a floating configuration, which improves the load 

distribution among the planets. To accommodate the floating sun arrangement, the low-speed 

shaft is hollow and has an internal spline that transfers the torsional loads to the parallel shaft 

stages. The low-speed planetary gears have a helix angle of approximately 7.5 degrees, and the 

intermediate speed and high speed gear sets have a helix angle of 14 degrees. 
13 

Figure 5 shows a schematic of the internal components of the test gearbox, with nomenclature 

used to describe them. Other than gears, several types of bearings are employed in the test 

gearbox, according to the loading conditions and gearbox life requirements. The planet carrier 

(PLC) is supported by two full-complement cylindrical roller bearings (fcCRB). Each planet gear 

is supported by two identical cylindrical roller bearings (CRB). Each parallel shaft (i.e., low, 

intermediate, and high speed) in the gearbox is supported by a CRB on the upwind (rotor) side of 

the assembly, and by two back-to-back mounted, tapered roller bearings (TRB) on the downwind 

(generator) side. Lubrication oil is another important component in the test gearbox, although it 

is not shown in either Figure 4 or Figure 5. The main functions of lubricant are to [26]: 1) 

provide a lubricating film to reduce friction and wear between moving parts; 2) cooling to

dissipate heat away from the critical parts of the equipment; 3) cleaning and suspending 

products, such as carbon, sludge and varnish, to facilitate smooth operation of equipment; and 4) 

protection to prevent metal damage due to oxidation and corrosion.

Figure 4. Exploded view of the GRC test gearbox

Figure 5. GRC gearbox layout and bearing nomenclatureTest Platforms 

PLC-A PLC-B

LSS-A LSS-B LSS-C

PL-A PL-B

ISS-A ISSBISS-C

HSS-A HSS-B HSS-C Annulus

Planet

Sun

Gear

Gear Pinion

Pinion
14 

There are two test platforms used in the GRC. One is the NREL 2.5 MW dynamometer test 

facility, where all dynamometer tests are conducted. The other is a field turbine located at Xcel 

Energy’s Ponnequin wind farm. 

The NREL 2.5-MW dynamometer test facility was developed to conduct performance and 

reliability tests on wind turbine drivetrain prototypes and commercial machines [27, 28]. The 

facility is capable of providing static, highly accelerated life and model-in-the-loop tests. The 

prime movers of the dynamometer are a 2.5-MW induction motor, a three-stage epicyclical 

reducer, and a variable-frequency drive, with full regeneration capacity. The rated torque 

provided by the dynamometer to a test article can be up to 1.4 meganewton meters (MNm), with 

speeds varying from 0 rpm to 16.7 rpm. Non-torque loading actuators, rated up to 440 

kilonewtons (kN) for radial load and 156kN for thrust load, also can be utilized in the 

dynamometer to apply thrust, bending, and shear loads similar to those typically generated by a 

wind turbine rotor. Figure 6 provides a diagram of the test facility. Figure 7 is a photo of the test 

implementation, with the test article installed.

Figure 6. Diagram of NREL 2.5-MW dynamometer test facility
15 

Figure 7. NREL dynamometer test stand with the test article installed. PIX #16913.

The GRC test turbine (Figure 8) is a three bladed, up-wind, stall controlled turbine, with a rated 

power of 750kW. The generator has two sets of poles, which allow it to operate at two speeds. 

The turbine rotor operates at 22.4 rpm (1,810 rpm on the HSS) and 14.9 rpm (1,208 rpm on the 

HSS). The turbine has "pitchable" tip brakes and a high-speed shaft brake. For a normal 

shutdown, the tip brakes deploy first. Once the rotor has been slowed down enough, the highspeed shaft brake engages. For an emergency stop, tip brakes and high-speed shaft brakes are

applied at the same time. For the transition from low speed to high speed, the turbine drops offline, the rotor speeds up, and the turbine comes on line when the generator shaft reaches 1,800 

rpm. For the transition from high speed to low speed windings, the turbine comes off-line and 

deploys the tip brakes to slow the rotor. Once the rotor is below the synchronous speed, the tips 

are returned to their un-deployed position and the rotor can accelerate again. The turbine comes 

online when the generator shaft reaches 1,200 rpm. 
16 

Figure 8. GRC test turbine at Xcel Energy's Ponnequin wind farm. PIX #19257.

Tests Conducted 

The two test gearboxes are referred to as Gearboxes 1 and 2 in this report. Since the beginning of 

the GRC project, various tests have been conducted [6]. For ease of discussion in the subsequent 

sections, those CM-related tests are listed in Table 2. 
17 

Table 2. GRC CM-related testing 

When Where Objective Designation

Apr - Jul 2009 2.5-MW 

dynamometer

Controller shake down and 

run-in

Phase 1 Gearbox1 

Dynamometer

Test

Sep - Nov 

2009

Ponnequin wind 

farm

Collection of field data Phase 1 Gearbox 

1 Field Test

Oct - Dec 

2009 

2.5-MW 

dynamometer

Run in, static non- torque 

loading in limited 

directions

Phase 1 Gearbox

2 Dynamometer 

Test

Jun - Aug 

2010 

2.5-MW 

dynamometer 

Static non-torque loading 

in any direction, dynamic 

non-torque loading and 

dynamic torque 

Expanded load cases

Phase 2 Gearbox

2 Dynamometer 

Test

Sep 2010 2.5-MW 

dynamometer

Compare as-built and 

damaged behavior 

Compare gearbox 1 and 2

Collect condition 

monitoring data on 

damaged gearbox

Phase 2 Gearbox 

1 Dynamometer 

Retest

During the Phase 1 Gearbox 1 Dynamometer Test, both gearboxes were run in the NREL 2.5-

MW dynamometer. The run-in was performed before any other operation to carefully condition 

the surfaces of the gear teeth. Several CM systems were used during the run-in to determine 

appropriate load level durations (see the results and discussions section). Since this was the first 

operation for each instrumented gearbox, it also was used for extensive signal checking and to 

establish base-line data for comparison of gearboxes 1 and 2 under identical controlled 

conditions. Gearbox 1 was run-in during Phase 1 testing by following a prescribed series of 

operating torque values. Each torque level was held until the level of wear particles in the lube 

oil system stabilized. Preheated run-in oil (lube oil lacking anti-wear additives) was used to 

accelerate the run-in process. 

During the Phase 1 Gearbox 1 Field Test, the gearbox was installed on July 16, 2009, in the test 

turbine at Ponnequin Wind Farm. The turbine was put in unattended operation on September 14, 

and the testing was stopped on October 5, 2009. During that period, more than 300 hours of data 

were recorded. During testing operations, the wind turbine faulted several times due to highspeed bearing temperatures exceeding 90o C. There also were two incidents of significant oil 

loss. An inspection on October 6, 2009, revealed that the high-speed stage gear teeth showed 

signs of significant overheating. It was determined that testing should be suspended to avoid the 

potential for catastrophic gearbox failure. Subsequently the gearbox was removed from the 

turbine and shipped back to NREL. 

During the Phase 1 Gearbox 2 Dynamometer Test, the gearbox was run-in using similarly 

prescribed torque points used during the Phase 1 Gearbox 1 Dynamometer Test. Additional oil 
18 

particle sensors were added to the CM system. Dyechem was not used during the run-in to 

prevent false wear particle readings. Non-torque load testing was performed to provide input data 

for modeling efforts. The test series consisted of a range of non-torque loads applied at fixed 

azimuths. Test article torque was varied for each case. The static non-torque load system was 

used due to lengthy component lead times on the dynamic non-torque system. 

During the Phase 2 Gearbox 2 Dynamometer Test, a series of tests was conducted, including a 

generator misalignment test, a non-torque loading test, and a static and cyclic thrust test. In 

addition, based on the torque time history obtained from the Phase 1 Gearbox 1 Field Test, the 

field torque time series was reproduced in the dynamometer and tested. By manipulating the 

trunnion mounts in a certain way, a carrier bearing clearance removal and fixed trunnion blocks 

test also were conducted. 

During the Phase 2 Gearbox 1 Dynamometer Retest, the damaged gearbox that was removed 

from the Ponnequin Wind Farm was retested in the NREL 2.5-MW dynamometer. NREL's

customized, vibration-based CM system was added to capture the vibration signature resulting 

from damage that occurred during the field testing. After conducting a limited set of CM tests in 

the NREL dynamometer, the gearbox was sent to a repair shop for disassembly and inspection.
19 

CM System Configurations 

At the different stages of the GRC tests, various CM systems were deployed. Table 3 gives a 

brief summary of the CM system configurations at different stages of the GRC tests. For more 

details on these packages, please check the CM Systems Investigated section. 

Table 3. CM system implementation at different stages of the GRC tests

Maker Model

Phase 1 

Gearbox 1

Dynamometer

Phase 1

Gearbox 1

Field

Phase 1 

Gearbox 2

Dynamometer

Phase 2 

Gearbox 2

Dynamometer

Phase 2

Gearbox 1

Dynamometer 

SKF

WindCon X X X X X 

Dynamic Motor 

Analyzer X X 

SwanTechSWANwind X X X X X 

Kittiwake

Online Sensor 

Suite X X X X X 

Particle Content 

Sensor X X X 

Macom TechAlert10 X X X X X 

GasTOPS MetalScan 3000 X X X 

Hydac CSM 1220 X X X X 

Eaton

Motor Condition 

Monitoring 

System

X X 

NREL Vibration-based 

CM System X 

For tests conducted in the dynamometer, the major change in the CM system configuration was 

the addition of new packages. The CM system configuration for Phase 2 Gearbox 1 

Dynamometer Retest is illustrated in Figure 9, where the different types of monitoring 

techniques that these packages represent are indicated. It is worth noting that both inline and 

offline particle counts are referred to as oil debris monitoring; offline oil condition measurement 

refers to total ferrous debris, oil quality and moisture. 
20 

Figure 9. CM system configuration in Phase 2 Gearbox 1 Dynamometer Retest 

Due to intellectual property constraints, detailed sensor locations for the commercial Stress 

Wave and Vibration-based monitoring systems cannot be disclosed. As a result, the setup used in 

NREL's customized vibration-based CM system is illustrated in Figure 10. It illustrates most of 

the typical accelerometer mounting locations for vibration-based CM systems seen in the wind 

industry today. The sensor notations are given in Table 4 and their physical installations are 

illustrated in Figure 11. It is worth noting that a typical commercial CM system uses only a 

portion of the 12 sensors listed in Table 4. One typical configuration consists of eight 

accelerometers: two on the main bearing, including one in the radial direction (AN1), and the 

other in the axial direction (AN2); four on the gearbox, including one for the planetary section in 

the radial direction (AN3 or AN4) and one for each stage of the gearbox, i.e. LSS (AN5), ISS 

(AN6), and HSS (AN7), all in the radial direction; and two on the generator, including one on the 

drive end (AN11) and the other on the non-drive end (AN12), both in the radial direction. The 

other typical configuration also consists of eight accelerometers and differs from the first by 

replacing AN5 through 7 with AN8 through 10. 

For real-time lubricant CM systems, where sensors are installed in both inline and offline filter 

loops, it is necessary to have a clear schematic of the filtration system to better understand their 

results. Figure 12 illustrates the schematics for the filtration system used during the Phase 2 

Gearbox 1 Dynamometer Retest, including the real-time lubricant CM sensors K1 to K3 (model 

information removed to avoid disclosing specific sensor suppliers), ISO cleanliness level
21 

Figure 10. NREL customized vibration-based CM system sensor locations 

Table 4. Sensor notations and descriptions 

Sensor Label Description

AN1 Main bearing radial

AN2 Main bearing axial

AN3 Ring gear radial 6 o’clock

AN4 Ring gear radial 12 o’clock

AN5 LSS radial

AN6 ISS radial

AN7 HSS radial

AN8 HSS upwind bearing radial

AN9 HSS downwind bearing radial

AN10 Carrier downwind radial

AN11 Generator drive end radial

AN12 Generator non-drive end axial
22 

 AN1, AN2, AN3, and AN4 (From left to right, PIX #19589, 19590, 19588, 19587) 

 

AN5, AN6, AN7, and AN8 (From left to right, PIX #19591, 19592, 19594, 19593) 

 

AN9, AN10, AN11, and AN12 (From left to right, PIX #19595, 19598, 19597, 19596) 

Figure 11. Accelerometer installation on NREL's customized vibration-based CM system 

measurement system, and sensors for measuring oil condition, specifically wear (total ferrous) 

debris, oil quality, and moisture. It includes an inline filter loop and an offline filter loop. Within 

the inline filter loop, sensor K1 is located on the gearbox suction line before a pump and is 

followed by a two stage filtration system for contamination removal and a heat exchanger for oil 

temperature control. Within the offline filter loop, sensor K2 is located on one of the two 

branched lines off the offline suction line, between the offline filter pump and its filter cartridge. 

Sensor K3 is located in the other set of two branched lines off the offline suction line. The ISO 

cleanliness level measurement system and the wear (total ferrous) debris, oil quality, and 

moisture measurement sensors are located on the same line. 
23 

Figure 12. Lubrication system schematics for Phase 2 Gearbox 1 Dynamometer Retest 
24 

For the Phase 1 Gearbox 1 Field Test, it should be noted that only the commercial Stress Wave, 

Vibration-based CM system, sensor K2, and sensors for measuring wear (total ferrous) debris, oil 

quality, and moisture were deployed. Also, the filtration system schematic is different from what 

is shown in Figure 12, as the inline filter loop was from the field test turbine. The offline 

filtration loop is similar to what is shown in Figure 12. 
25 

Test Results and Discussions

CM data was collected during both phases of testing. This section presents some of these 

diagnostic results, according to various investigated CM techniques. As the majority of the 

results to be presented were obtained during the Phase 2 Gearbox 1 Dynamometer Retest, the 

damage incurred by Gearbox 1 is highlighted here. Figure 13. shows a picture of the high speed 

stage taken while the gearbox was in the field after the second oil loss. Figure 14, which was 

obtained during its later disassembly, illustrates the high speed stage gear damage. The main 

cause for such damage was the two oil loss events experienced by the test gearbox in the field, as 

mentioned previously. 

 

Figure 13. Gearbox 1 high speed stage (taken in field after second oil loss event). PIX #19600.

Figure 14. Gearbox 1 high speed stage gear damage (during Gearbox 1 disassembly). PIX #19599.

The test results are presented with a focus on the diagnosis of the damage to test Gearbox 1 

compared to the healthy Gearbox 2. 

Vibration Analysis

One key element in various commercial vibration-based CM systems is data analysis algorithms. 

The commonly used algorithms can be classified into two categories: time and frequency 

domains. For time domain techniques, the monitored parameters may include peak, root mean 

square (RMS), crest factor (i.e., the ratio between the amplitude and RMS within a defined time 

window), peak-to-peak interval, mean, standard deviation, skew, and kurtosis [29, 30]. For 
26 

frequency domain techniques, the common practices are standard fast Fourier transform (or 

spectrum), order analysis, envelope analysis (or amplitude demodulation), and side band analysis 

[29]. The time domain parameters are normally used to monitor the trend of overall vibration 

level over time, at a specific measurement location. Time interval-based or vibration level-based

triggering mechanisms can be set up based on the time domain parameter that reflects overall 

vibration trending. Once triggered, a discrete frequency analysis snapshot can be taken. Based on 

these snapshots, detailed examinations of gearbox health can be conducted. Also, the amplitude 

of characteristic frequencies for gears (e.g., meshing frequency) and bearings (e.g., ball passing 

frequency) can be trended over time to detect potential failures [13]. 

Figure 15 and Figure 16 show sample results obtained by the investigated vibration-based CM 

system from the GRC tests [31]. Figure 15 results were obtained during the Phase 1 Gearbox 2 

Dynamometer Test. Figure 16 results were obtained during the Phase 2 Gearbox 1 Dynamometer 

Retest. They are spectra of vibration data collected by one accelerometer mounted to the 

Intermediate Speed Shaft on the back cover of the test gearbox. Figure 15 shows measurements 

from the healthy gearbox, while Figure 16 shows data collected from the damaged gearbox. For 

both figures, the horizontal axis shows frequency in counts per minute (60 cpm = 1 Hz) and the 

vertical axis shows acceleration in g (1 g=9.8 m/s^2). The fundamental high speed stage gear 

meshing frequency (GMF) of 39,600 cpm or 660 Hz, and its second harmonic are labeled in the 

figures. When comparing the figures, the damaged gearbox clearly has more frequency 

components and elevated amplitude for GMF sidebands. For illustration purposes, the ten 

sideband frequencies (five below and five above) of the second harmonic in the high speed stage 

GMF are labeled in Figure 16. The spectrum pattern illustrated in Figure 16 typically represents 

abnormal gear set behaviors. When the sideband frequencies are around 39,600 cpm, it is clear 

that the problem occurs during the high speed stage. In addition, the amplitude of the 

fundamental high speed stage GMF increased from 0.0325 g in the healthy gearbox to 0.0875 g 

in the damaged gearbox. Such an increase in GMF amplitude is another indication of abnormal 

gear set behavior. Even without a detailed calculation, it is obvious that the energy contained in 

the frequency spikes from the damaged gearbox is much higher than that of the healthy gearbox. 

This is a result of both increased amplitude of those frequencies already contained in the healthy 

gearbox and the additional energy caused by new frequency components in the damaged 

gearbox. Based on these findings, it is reasonable to conclude that the vibration-based 

monitoring technique can successfully diagnose such gear set damage. 
27 

Figure 15. Results from a vibration-based CM system (healthy gearbox) 

Figure 16. Results from a vibration-based CM system (damaged gearbox)
28 

Stress Wave Analysis

Another popular CM technique used by the wind industry is called stress wave analysis [32]. It 

uses specially designed stress wave sensors to pick up the stress waves (acoustic emissions) 

generated by the frictional and strike events in a monitored structure. Most of the vibration 

analysis algorithms mentioned earlier also are applicable to the analysis of stress wave time 

series. One unique result provided by the stress wave technique is called a stress wave amplitude 

histogram, which is discussed below. 

Figure 17 and 18 show the results obtained by the AE-based CM technique (specifically, stress 

wave). The stress wave sensor was mounted axially in the middle of the three shaft ends on the 

back of the test gearbox. The sensor output is conditioned to generate a stress wave pulse train, 

which represents a time history of individual shock and friction events that have occurred in the 

monitored machine [15, 32]. By first finding the peak amplitude of each of the pulses in the 

stress wave pulse train, and then distributing these peaks into voltage bins that correspond to the 

value of each reading, the resultant diagram—called a Stress Wave Amplitude Histogram [32]—

is generated. Two are shown in Figure 17 and Figure 18. In healthy machines, the distribution 

should be a narrow bell shape located at the lower end of the voltage scale, as illustrated in 

Figure 17. In abnormal machines, the distribution should be much broader and shifted to the 

right on the amplitude scale, as illustrated in Figure 18. Figure 17 was obtained during the Phase 

1 Gearbox 2 Dynamometer Test and Figure 18 was obtained during the Phase 2 Gearbox 1 

Dynamometer Retest. Figure 17 implies that the monitored gear sets were healthy and Figure 18

implies that the monitored gear sets were problematic. To identify whether the damaged gear set 

is at high speed, or at the intermediate speed stage, it is necessary to obtain some frequency 

domain information. Because spectra of stress wave time series are expected to be similar to 

those obtained from the vibration-based technique, they are not presented. 

Figure 17. Stress Wave Amplitude Histogram (healthy gearbox) 
29 

Figure 18. Stress Wave Amplitude Histogram (damaged gearbox) 

Oil Monitoring 

Oil monitoring is typically applied to the wind turbine gearbox, as it is the only oil-lubricated 

component in the drivetrain. The objective of oil monitoring is to detect oil contamination and 

degradation [33]. Oil contamination can be caused by dirt, wear debris, water, and the use of the 

wrong oil. Degradation can result from depletion of additives, oxidation, and base stock 

breakdown. Oil monitoring can help detect lubricant, gear, and bearing failures and is an 

important factor in achieving maximum service life for wind turbine gearboxes [26]. In this 

report, oil monitoring techniques are divided into: oil cleanliness, oil debris monitoring, oil 

condition monitoring, and offline oil sample analysis. The oil cleanliness level test counts 

particles down to 4 μm. The oil debris monitoring sensors count particles down to the tens of μm 

level. These sensors can provide cumulative particle counts, as well as ferrous and nonferrous 

particles in different size bins. The oil condition monitoring sensors, on the other hand, measure 

total ferrous debris, oil moisture, and changes in oil quality caused by the contamination level, 

the acidic level, and the water content. Oil sample analysis first involves taking an oil sample 

from the gearbox lubrication system and then sending the sample to a dedicated laboratory for 

analysis. An analyst at the oil analysis laboratory reviews the results and provides 

recommendations to the owner/operator of the test turbine. The main reasons to include oil 

sample analysis, in addition to the oil cleanliness level, debris and condition monitoring are [33] 

to: 

• Monitor parameters not already covered 

• Conduct elemental analysis so that failed components can be indentified

• Assist root cause analysis for some component failures. 
30 

The typical parameters sought in an oil sample analysis include [34] particle counts, water 

content, total acid number, viscosity, and particle element identification. The recommended 

interval for oil analysis, as stated by wind turbine manufacturers, is typically one sample every 

six months. However, if the oil cleanliness, debris, or condition sensors reveal abnormal 

conditions, it is better to conduct a spot oil sample analyses, which may help in identifying 

component failures in progress. The results obtained during various stages of the GRC tests by 

these four oil monitoring techniques are presented below. 

Oil Cleanliness Level Monitoring 

The first subset of oil monitoring results was the oil cleanliness level measured during the Phase 

1 Gearbox 1 Dynamometer Test, when the test gearbox was run-in at 25% of rated load level. 

The generator status is shown in Figure 19. The figure's horizontal axis is time and the vertical 

axis is the ISO 4406:1999 cleanliness code [17]. It is divided into three stages: around 13:51, the 

generator speed started ramping up; around 13:59, the generator was connected to the grid; and 

around 14:10, the generator went off the grid, but the lubricant CM system was left running. At 

around 14:34, the power supply to all CM systems shut off and all oil cleanliness level readings 

dropped to zero. This is not shown in the figure. As shown in Figure 19, a broad range of particle 

sizes were generated throughout the gearbox operational process, which is demonstrated by the 

increased oil cleanliness level in all three bins (> 4 μm, > 6 μm, and > 14 μm). This test verified 

that wind turbine gearboxes always emit particles. It is worth noting that the larger the number, 

the more contaminated the oil. The oil cleanliness level decreased when the generator speed was 

ramped up and increased when the generator was shut down and the offline filter loop filtration 

system was left running. Based on this observation, it can be inferred that the oil cleanliness level 

has the potential to be used to monitor and control the run-in of wind turbine gearboxes. The 

rationale is that the oil cleanliness level will increase when the run-in begins at a certain load 

level. As the run-in continues, the measured cleanliness level will gradually stabilize because

contacted surfaces are smoothed through run-in and the continuously functional lubricant 

filtration system. These results can be used to determine when run-in of wind turbine gearbox 

may occur under a certain load level. 

Oil cleanliness level results similar to Figure 19 also were obtained during the Phase 1 Gearbox 2 

Dynamometer Test, when the test gearbox was run-in. Test data from both test gearboxes 

indicate that the cleanliness readings did, in fact, fall off at the 1+ hour range, depending on load 

and temperature. The industry has recognized that using cleanliness is a better approach than the 

time-at-load level since it verifies that run-in has actually occurred, and these results are good 

evidence to support that assumption. 
31 

Figure 19. Oil cleanliness level (run-in) 

Oil Debris Monitoring 

The second set of oil monitoring results was obtained from the oil debris monitoring sensors. 

Several groups of results are presented, with the intention to cover different aspects of the oil 

debris monitoring technique. 

Figure 20-22 show results obtained during the Phase 1 Gearbox 2 Dynamometer Test, when the 

test gearbox was being run-in. The counts represent the total number of particles, ferrous or 

nonferrous, with a size greater than a certain size, varying from 35 μm to 300 μm depending on 

the sensor being used, and detected by the sensors throughout the entire test period. Test dates 

are plotted on the horizontal axis and the vertical axis plots cumulative particle counts detected 

by sensors K1 to K3. We observed that the total counts, at the end of the test, varied among the 

three sensors. The implication is that the sensor outputs are affected by measurement locations. 

However, their trends are similar. Note that sensor K1 is located in the inline filter loop and 

sensors K2 and K3 are located in the offline filter loop. 
32 

Figure 20. Cumulative particle counts obtained by sensor K1 (run-in)

Figure 21. Cumulative particle counts obtained by sensor K2 (run-in)

0 

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33 

Figure 22. Cumulative particle counts obtained by sensor K3 (run-in)

Figure 23 shows the oil debris particle counts obtained during the Phase 2 Gearbox 1 

Dynamometer Retest by sensor K1. The horizontal axis shows the date and the vertical axis lists 

the cumulative particle counts. Within three calendar days, the counts were increased from 0 to 

about 680. More specifically, the particle generation rate reached about 70 particles per hour on 

September 16. In comparison, about 11 particles were generated over a period of four hours from 

a healthy gearbox [35]. It is, therefore, reasonable to conclude that under similar environmental 

conditions, such as oil temperature, shaft rotational speed, and driven torque, a damaged gearbox 

has higher particle-generation rates than a healthy gearbox. It is worth noting that the lubricant 

used in this test is different from the oil used during the run-in, which does not have anti-wear 

additives. However, due to the lack of frequency information from the oil debris monitoring 

technique, it is difficult to tell where the damage occurred.

Figure 23. Cumulative particle counts obtained by sensor K1 (damaged gearbox) 

0 

100

200

300

400

500

600

700

800

9/15 9/16 9/17 9/18 Date
34 

Figure 24 and 25 show the ferrous particle counts obtained from sensors K2 and K3 during the 

Phase 1 Gearbox 2 Dynamometer Test, when the test gearbox was being run-in. The horizontal 

axis of each figure is the date and the vertical axis shows particle counts. Each plot has five 

curves, which represent five different size bins. Assuming the test gearbox at the run-in stage 

was healthy, even large particles (greater than 100 μm) are generated. It is, therefore, not correct 

to claim large particles are only generated when the monitored gearbox has damaged internal 

components. On the other hand, since the counts within each size bin also vary, this demonstrates 

that the location of the sensor influences the readings. 

 

Figure 24. Ferrous particle counts by sensor K2 categorized into five size bins (run-in)

Figure 25. Ferrous particle counts by sensor K3 categorized into five size bins (run-in)
35 

Oil Condition Monitoring

The third sub set of oil monitoring results was obtained from the oil condition monitoring 

sensors, specifically total ferrous debris, oil quality, and moisture. The measurement was made 

in the offline filter loop. Figure 26 shows the results obtained during the Phase 1 Gearbox 1 Field 

Test. Figure 27 shows the results obtained during the Phase 2 Gearbox 1 Dynamometer Retest. 

The horizontal axis for both figures is the test date. The units for four monitored variables are: 

• Oil Temperature – Celsius

• Box (i.e. sensor package) Temperature – Celsius 

• Oil Moisture – % 

• Oil Total ferrous debris – parts per million (ppm) 

There was another channel on oil quality, but it is not shown in these two figures as no 

significant changes were observed. In addition, it should be pointed out that the test experiences 

on the oil condition sensors, from both the field test and the dynamometer retest of Gearbox 1, 

are inconclusive. However, there are still several points worth mentioning. 

Figure 26 shows that total ferrous debris has several spikes, which may be an indication of 

possible gearbox damage. The moisture was decreasing, which was reasonable since oil loss 

should have led to elevated oil temperature making the oil drier. However, wind speed 

information is needed to confirm whether the two temperature channels match the turbine

operational conditions. Assuming a match, the data has demonstrated the complex dynamics that 

wind turbines may experience in the field. 

Figure 26. Oil condition sensor readings (field test)
36 

In Figure 27, the total ferrous debris channel also showed several spikes, which may be 

considered an indication of possible gearbox damage. It should be pointed out that the amplitude 

of these spikes increased as the test progressed. The reason is that both the testing load and shaft 

rotational speed were intermittently increased from the first to the last day of the test. The 

moisture level did not change much, which might be due to the short testing period. These much 

cleaner curves, with respect to those shown in Figure 26, demonstrate that the test conditions in 

the dynamometer were less dynamic, since they were purposely controlled. 

Figure 27. Oil condition sensor readings (dynamometer retest)

Oil Sample Analysis 

The fourth set of oil monitoring results was obtained by oil sample analysis. The extensive 

analyses included not only the typical parameters discussed earlier, but also some very 

sophisticated procedures like such as Scanning Electron Microscope (SEM) analysis. For 

illustration purposes, one set of chemical element identification results obtained during the Phase 

1 Gearbox 2 Dynamometer Test. They are shown in Figure 28 [33]. The reference limits were set 

by the oil analysis laboratory based on historical data collected from similar wind turbine 

gearboxes. The columns in Figure 28 correspond to oil samples. The first column in the analysis 

results section shows data from a new oil drum. The next five columns in the analysis results 

section were taken from the lubrication system during gearbox testing. When there is component 

damage, some metal contents in the operational samples may show a rising trend, or exceed the 

reference limits. In this case, the analyzing laboratory will notify the owner/operator of the 

turbine and recommend maintenance actions. Based on the chemical element results, and the 

known chemical compounds that make up the test gearbox' internal components, identification of 

the damaged component can be determined. Further evaluation of the particle shape and 
37 

morphology make it possible to infer failure modes and can help in root cause analysis 

determinations [36]. 

Figure 28. Oil sample analysis (run-in)

Electrical Signature Analysis

Preliminary analysis of the currents and voltages collected during the Phase 2 Gearbox 1 

Dynamometer Retest did not show any indication of gearbox damage. One possible reason is that 

the damage in the test gearbox was not severe enough for the electrical signature analysis 

technique to detect. Therefore, no detailed results were reported. However, this does not imply 

that electric signal analysis is not potentially useful for wind turbine drivetrain or generator CM. 

Summary of Results 

The various results presented demonstrate that: 

• The spectrum analysis of the vibration signal (or stress waves) can distinguish between 

healthy and damaged gearboxes, and, to a certain extent, pinpoint the location of damaged 

gearbox components. The diagnosis can determine which stage of the monitored gearbox has 

damage, but it may not be able to specify which bearing or gear, since several bearings or 

gears may have the same characteristic fault frequencies. 

• The stress wave amplitude histogram appears to be effective for detecting gearbox abnormal 

health conditions.

• Oil cleanliness level can be used to control and monitor wind turbine gearbox run-in. The 

typical run-in interval observed in the GRC tests was in the 1+ hour range for each load level. 

• Oil debris particle counting is effective for monitoring gearbox component damage, but is not 

effective for pinpointing damage location. 

o Readings are affected by sensor mounting locations. 
38 

o Similar particle counting trends can be obtained between the inline filter (main) and the 

offline filter (kidney or side stream) loops. 

• A damaged gearbox releases particles at increased rates. 

• Oil condition monitoring results, specifically moisture, total ferrous debris, and oil quality, 

indicate that oil total ferrous debris appears indicative of gearbox component damage. More 

data is required to evaluate the measurements of oil moisture and quality. 

• Periodic oil sample analysis may help pinpoint a failed component and support root cause 

analysis. 

• Electrical signature-based techniques so far have not distinguished between the healthy and 

damaged gearboxes. 
39 

Challenges in Wind Turbine Drivetrain CM and Future R&D Areas 

Based on the drivetrain CM research conducted so far under the GRC, it is clear that there is 

room to improve typical O&M practices adopted by wind plant owner/operators. CM is one 

enabling technique for such improvements to help reduce O&M costs and, subsequently, the 

COE for the wind industry. When turbines are installed offshore, the improvements in CM 

techniques used broadly in O&M practices will become even more important. 

Among the various CM techniques investigated in this research, vibration or stress wave analysis 

and oil monitoring CM techniques have been successfully applied, and are increasingly 

deployed, in the wind industry. However, there are still challenges facing these monitoring 

techniques, which must be addressed. The first challenge for wind turbine CM, as a whole, is the 

justification of cost benefits. Relative to other traditional power generation industries where CM 

is widely adopted, the revenue stream from each wind turbine may be an order of magnitude 

lower. 

Challenges to Implementation of Wind Turbine Drivetrain CM 

For vibration, or stress wave-based CM technique, the following list can be used as a starting 

point for understanding the challenges [9, 37]: 

• Accessibility. Limited machine accessibility makes it challenging to retrofit wind turbine CM 

systems. 

• Measurement strategy. The type, number, and location of sensors will affect the 

measurement, and subsequently, the analysis results. In addition, the configuration of 

complex gearboxes varies, making it difficult to determine a cost-effective and universal 

solution. 

• Diagnostics. Changing wind direction and speed cause variable speed and load conditions 

when collecting vibration data. Compared to stall-regulated wind turbines, variable speed 

wind turbines have additional complexities. In addition, it can be challenging to directly 

apply vibration-based CM systems used in other industries to the wind industry due to low 

rotor speeds. 

• Data interpretation. Currently, a vibration-based CM system normally generates an extensive 

amount of vibration data, which typically requires expert assistance to interpret and provide 

recommendations for maintenance. 

• Condition-based maintenance (CBM). Online drivetrain vibration monitoring is not yet ready 

for CBM because there is still uncertainty about severity levels and prognostics of 

degradation. 
40 

Some recommendations for best practices to address these challenges are: 

• An individual vibration-based CM system setup for each turbine is recommended to handle 

the variations caused by a number of factors, including sensor location, gearbox 

configuration, and blade condition [38]. 

• To handle the variation in wind and turbine operational speeds, it is important to have an 

accurate shaft rotational speed measurement [12]. 

In addition, new areas of research have to be conducted, such as developing: 

• New sensing technologies, such as Spectral Emitted Energy (SEE) sensors [38] 

• New diagnostic algorithms, such as the synthesized synchronous sampling method [39].

For oil monitoring, consideration must be given to the many factors affecting oil debris, 

condition sensor readings, and oil sample analyses [40]. Some of these factors also can be 

considered as challenges. For oil debris and condition monitoring, the challenge is to correctly 

interpret the sensor readings (e.g., oil quality and moisture), as operational conditions have 

significant impacts. These include temperature changes that occur from stop to start in day-today turbine operations and also cross-seasonal operations. To mitigate this challenge, speed and 

temperature information can be integrated when interpreting the oil debris and condition 

monitoring sensor readings [40]. For oil sample analysis, there is no single standard for all wind 

turbines [40]. Different turbine manufacturers, or lubrication oil suppliers, may require a unique 

set of analyses to obtain credible results. Other challenges include: 

• Obtaining a representative sample. There is no access to the gearbox when a turbine is in 

operation. 

• Inconsistent sampling practices. Oil samples might be taken at different ports and oil settling 

conditions may vary in the same gearbox.

• Incomplete information in samples sent for analysis. Some missed information may include 

oil time, gearbox model, and oil change records. 

Standardized tests of lubrication oil, if possible, should be specified to address these challenges. 

In addition, education and training are needed for practices such as taking representative oil 

samples and proper labeling. 
41 

Future R&D areas in Wind Turbine Drivetrain CM

Although the CM technologies face various challenges in wind turbine applications, they are still 

valuable and beneficial to the entire wind industry. 

Below are opportunities for improvements identified for advancing wind turbine CM: 

• Determine the most cost-effective measurement or monitoring strategy

• Improve the accuracy and reliability of diagnostic decisions, including severity level 

evaluations 

• Automate the “expert” in data interpretation to make actionable recommendations automatic 

• Develop reliable and accurate prognostics techniques.

Additional opportunities of improvement include: 

• Improved use of Supervisory Control and Data Acquisition (SCADA) system data, which is 

normally only stored at 10-minute intervals

• Performance monitoring, usage monitoring, and load estimation [41] to help prognostics 

• Fleet-wide condition monitoring and asset management [5] 

• Root cause analysis to help improve the turbine operation, control strategy, and component 

design. 

When turbines are installed offshore, the scope of CM must be expanded from the baseline 

onshore turbine studied here to include additional subsystems, such as undersea transmission 

lines. The influences of water on turbine component health need to be examined. The load 

estimation becomes more complex, as both wave and wind influences are involved. As a result, 

novel sensing or sensor integration strategies may need to be developed [42]. Maintenance 

strategies also may need to be enhanced by integrating them with improved forecasting [42]. 
42 

Conclusions 

A fixed duration at-load approach has been traditionally used for conducting the run-in tests of 

wind turbine gearboxes. The GRC run-in tests have shown that ISO cleanliness measurements 

should be used to monitor and control the run-in of wind turbine gearboxes. The ISO cleanliness 

levels, especially the readings from the 14 μm bin, increased with the start up of the run-in at a 

certain load level and the readings gradually leveled off at a longer duration than standard run-in 

procedures. This data can be used to determine when to stop the run-in at each load level, 

thereby achieving a complete run-in of the gearbox. If the run-in is not fully completed, surface 

roughness remains excessive and contact stresses are increased when the gearbox is placed into 

service. Increased contact stresses can result in premature failures of the gearbox.

Wind plant owner/operators may be under the misconception that one CM system can detect 

more problems in wind turbines than is possible. The GRC tests have demonstrated that different 

monitoring techniques can reveal different details of the drivetrain components. As a result, an 

integrated approach is recommended for the best possible results. 

Both damaged and healthy gearboxes were tested in controlled dynamometer tests and vibration, 

acoustic emission, electrical signature analysis, and oil monitoring data were acquired. The 

damage to the gearbox consisted of severe scuffing to the high speed stage gear and pinion. The 

data from each technique was evaluated in order to determine if the damaged gearbox could be 

clearly distinguished from the healthy gearbox. 

Vibration, acoustic emission, and oil debris monitoring all demonstrated the capability of 

distinguishing between the healthy and damaged gearbox components. Electrical signature 

analysis did not show any indication of the gear teeth damage, most likely because the damage to 

the gear teeth was not severe enough to significantly affect the generator currents. 

For oil debris monitoring, different vendors have different claims on the effectiveness of 

mounted sensors, in either the inline or offline filtration loops. The GRC test results indicate that 

the wear debris count appears effective for monitoring the gearbox, but measurements are 

affected by sensor mounting locations. If the sensor mounting location is appropriate, similar 

trends in wear debris counts, between the offline filter loop and the inline filter loop, can be 

obtained. 

To the authors’ knowledge, there are limited results for wind turbine CM testing that are publicly 

available to the industry. This research is one of the first attempts to provide this information and 

the research results demonstrate the value of a project being funded by a U.S. government 

agency and the benefits of collaboration with industry partners.
43 

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20, 2010. NREL Report No. PR-500--48289. Golden, CO: NREL. 

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11. C. Walford, Estimating the Benefits of CM for Wind Turbines, 2009, Wind Turbine 

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12. T. Francisco, Wind Farm Remote Monitoring, 2009, Wind Turbine Condition Monitoring 

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14. Kessissoglou, N. J. and Z. Peng. March 2003. “Integrating Vibration and Oil Analysis for 

Machine Condition Monitoring,” Practicing Oil Analysis Magazine. 

15. Board, D. B. July 2003. “Stress Wave Analysis Provides Early Detection of Lubrication 

Problems,” Practicing Oil Analysis Magazine. 
44 

16. Barnes, M. March 2008. “Oil Analysis: 5 Things You Didn't Know,” Reliable Plant 

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17. International Organization for Standardization. 1999. “ISO 4406: 1999 Hydraulic Fluid 

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19. Scientech – StressWave Systems (formerly SwanTech), Monitoring Wind Turbine 

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http://scientech.cwfc.com/products/PDFs/SWAN/MonitoringWindTurbinesDrivetrains.pdf 

20. Kittiwake, Kittiwake Online Sensor Suite, 

http://www.kittiwake.com/online_sensor_suite.htm

21. Kittiwake, Kittiwake Particle Content Sensor, 

http://www.kittiwake.com/particle_content_sensor.htm

22. Macom Condition Monitoring, Macom TechAlert 10 Brochure, 

http://www.macom.co.uk/images/stories/brochures/ta10.pdf 

23. GasTOPS, Metalscan On-line Oil Debris Monitor for Wind Turbines, 

http://www.gastops.com/pdf/MetalSCAN_3000_GasTOPS.pdf 

24. Hydac USA, Hydac CSM 1000 Seriese Brochure, 

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25. SKF, SKF Dynamic Motor Analyzer EXP 4000 Brochure, 

http://www.skf.com/files/883421.pdf 

26. M. Graf, Wind Turbine Gearbox Lubrication: Performance, Selection and Cleanliness, 2009, 

Wind Turbine Condition Monitoring Workshop, October 8–9, 2009, Broomfield, CO. 

27. Musial W, McNiff B. Wind turbine testing in the NREL dynamometer test bed. WindPower 

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28. National Renewable Energy Laboratory. 2.5 MW Dynamometer Testing Facility Fact Sheet 

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Processing, Vol. 20, Pages: 1483–1510. 

31. Ziegler, G. 2011. “General Observations SKF Condition Monitoring System Update,” 

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32. D. Board, Stress Wave Analysis Provides Early Detection of Lubrication Problems, 2003, 

Machinery Lubrication. [Online] http://www.machinerylubrication.com/Read/501/stresswave-analysis 
45 

33. S. Sheng, Investigation of Oil Conditioning, Real-time Monitoring and Oil Sample Analysis 

for Wind Turbine Gearboxes, 2011, AWEA Project Performance and Reliability Workshop, 

January 12–13, 2011, San Diego, CA. 

34. D. Troyer and J. Fitch, Oil Analysis Basics, 2001, Noria Corporation, Tulsa, OK. 

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Conference, May 22-25. 

36. Sheng, S.; Herguth, W.; Drake, T. (2011). “Investigation of Image-Based Particle Shape and 

Size Analysis Techniques for Wind Turbine Gearbox Lubricants.” Presented Society of 

Tribologists and Lubrication Engineers (STLE) 66th Annual Meeting and Exhibition, 

Atlanta, GA, May 15-19, 2011. NREL Report No. AB-5000-51073. Golden, CO: NREL. 

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Techniques, 2009, Wind Turbine Condition Monitoring Workshop, October 8–9, 2009, 

Broomfield, CO. 

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46 

Appendix A – Project Partners

Brüel & Kjær Vibro A/S

Castrol Industrial North America Inc. 

CC Jensen Inc.

Colorado School of Mines 

Eaton Corp. 

GasTOPS

GE Bently Nevada 

Herguth Laboratories, Inc. 

Impact Technologies 

IVC Technologies 

Kittiwake Americas

The Lubrizol Corporation 

Macom Condition Monitoring 

National Instruments

NRG Systems Inc.

Purdue University 

Schenck Corporation 

Sentient Corporations 

SKF

SKF Baker Instrument 

STC Consultants (SKF)

Scientech (formerly SwanTech) 

University of Cincinnati 

University of Connecticut 

University of Iowa 

University of New South Wales in Australia

Wichita State University
47 

Appendix B – Other Activities 

Workshop 

In response to the industry’s growing interest in CM, NREL held a wind turbine condition 

monitoring workshop on October 8-9, 2009, in Broomfield, CO. The workshop covered a broad 

range of topics: economic benefits, current CM practices, drivetrain monitoring, lubricant 

conditioning and monitoring, structural health monitoring, research and development efforts, and 

CM practices in other industries. Thirty-three experts in the field of CM were invited to moderate 

and present at the workshop, which was attended by 225 people. An overview paper (Sheng & 

Veers 2011) was written based on selected presentations given at the workshop. To better serve 

the industry, NREL plans to host a CM workshop every other year. 

Round Robin Project

Another activity launched along the line of CM is a wind turbine gearbox CM round robin 

project. In the field test, the GRC Gearbox 1 experienced a loss of oil on two separate occasions 

that resulted in damage to internal bearings and gear elements. It was determined that further 

tests of this gearbox in the field would cause more harm than benefit to the GRC project and tests 

on the gearbox were terminated. However, from the CM point of view, the damaged gearbox 

provided a unique opportunity to evaluate different CM technologies. The NREL GRC team 

successfully completed the retest of this gearbox under Phase 2 in the NREL 2.5-MW 

dynamometer. Various CM techniques were applied during the retest. The data collected by a 

customized vibration-based data acquisition system enabled NREL to launch a GRC Condition 

Monitoring Round Robin Project, which is currently underway. The main objective of this 

project is to evaluate different vibration analysis algorithms used for vibration-based wind 

turbine CM and determine the best practices. Another objective is to assess the capability of 

vibration-based CM and to establish a baseline from which improvements can be measured. The 

study includes 16 participating partners from three continents. They represent seven universities 

and 9industry partners. The project is unique in that it is a blind study, meaning the participants 

will not see the real damage information until their analysis results are submitted to NREL. At 

the date of this publication, the results submission period has ended. NREL has also completed 

the analysis results comparison and a further analysis by partners has begun. 
48 

Appendix C – Publications and Presentations 

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

Damaged Wind Turbine Gearbox.” Eighth International Workshop on Structural Health 

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

Sheng, S. (2011). “Wind Turbine Drivetrain Condition Monitoring.” Keynote Speech Presented 

at Pennsylvania State University Ben Franklin Center of Excellence in Structural Health 

Monitoring Annual Meeting, University Park, PA, August 4, 2011. 

Sheng, S. (2011). “Investigation of Various Wind Turbine Drivetrain Condition Monitoring 

Techniques.” Presented at Sandia Reliability Workshop, Albuquerque, NM, August 2-3, 2011. 

Report No. PR-5000-52352. 

Dempsey, P.; Sheng, S. (2011). Investigation of Data Fusion Applied to Health Monitoring of 

Wind Turbine Drivetrain Components. Report No. PR-5000-51467. Golden, CO: NREL. 

Sheng, S.; Herguth, W.; Drake, T. (2011). “Investigation of Image-Based Particle Shape and Size 

Analysis Techniques for Wind Turbine Gearbox Lubricants.” Presented Society of Tribologists 

and Lubrication Engineers (STLE) 66th Annual Meeting and Exhibition, Atlanta, GA, May 15-

19, 2011. NREL Report No. AB-5000-51073. Golden, CO: NREL.

Sheng, S.; Veers, P. “Wind Turbine Drivetrain Condition Monitoring – An Overview.” 

Machinery Failure Prevention Technology (MFPT): The Applied Systems Health Management 

Conference 2011. Virginia Beach, VA, USA, May 10-12, 2011. 

Sheng, S. (2011). “GRC Condition Monitoring Overview and Some Preliminary Results.” 

Presented at GRC All Member Meeting, Golden, CO, February 16-17, 2011. 

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

Analysis for Wind Turbine Gearboxes.” The American Wind Energy Association (AWEA) 

Project Performance and Reliability Workshop. San Diego, CA, USA, January 12-13, 2011. 

Report No. PR-5000-50301. Golden, CO: NREL. 

Sheng, S. (2010). “Investigation of Oil Conditioning and Monitoring Techniques for Wind 

Turbine Gearboxes.” The Society of Tribologists and Lubrication Engineers (STLE) 65th Annual 

Meeting and Exhibition. Las Vegas, NV, USA, May 16-20, 2010.

Sheng, S. (2010). “GRC Condition Monitoring Overview and Preliminary Results from Oil 

Condition Monitoring.” Presented at GRC All Member Meeting, Golden, CO, February 2-3, 

2010. 

Sheng, S.; Butterfield, S.; Oyague, F. (2009). “Investigation of Various Wind Turbine Drivetrain 

Condition Monitoring Techniques.” Presented at the 7th International Workshop on Structural 

Health Monitoring 2009, Stanford, CA, September 9-11, 2009. NREL Report No. CP-500-

46160. Golden, CO: NREL. http://www.nrel.gov/docs/fy10osti/46160.pdf. 
49 

Butterfield, S.; Sheng, S.; Oyague, F. (2009). “Wind Energy's New Role in Supplying the 

World's Energy: What Role Will Structural Health Monitoring Play?” Presented at the 7th 

International Workshop on Structural Health Monitoring 2009. Stanford, CA, September 9-11. 

NREL Report No. CP-500-46180. http://www.nrel.gov/docs/fy10osti/46180.pdf. 

Sheng, S. (2009). “Investigation of Various Wind Turbine Drivetrain Condition Monitoring 

Techniques.” Presented at 2009 Wind Turbine Condition Monitoring Workshop, Broomfield, 

CO, October 8-9, 2009. 

Sheng, S. (2009). “Overview of GRC Condition Monitoring.” Presented at Sandia Reliability 

Workshop, Albuquerque, NM, June 17-18, 2009. 

http://www.sandia.gov/wind/2009Reliability/PDFs/Day2-06-ShawnSheng.pdf. 

Sheng, S. (2009). “GRC Condition Monitoring.” Presented at GRC All Member Meeting, 

Golden, CO, January 22, 2009.