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

Investigation of Various 

Condition Monitoring 

Techniques Based on a 

Damaged Wind Turbine Gearbox

S. Sheng 

Presented at the 8th International Workshop on Structural Health 

Monitoring

Stanford, California

September 13 – 15, 2011

Conference Paper

NREL/CP-5000-51753

October 2011
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1

INVESTIGATION OF VARIOUS CONDITION MONITORING 

TECHNIQUES BASED ON A DAMAGED WIND TURBINE GEARBOX

Shuangwen Sheng

National Renewable Energy Laboratory

1617 Cole Boulevard, MS 3811

Golden, CO 80401

Telephone: (303) 384-7106

Shuangwen.sheng@nrel.gov

ABSTRACT

The National Renewable Energy Laboratory presented an investigation of 

various wind turbine drivetrain condition monitoring (CM) techniques at the 7th

International Workshop on Structural Health Monitoring held in 2009. The study 

contained in this report is a continuation of the 2009 paper and focuses on results 

obtained by various CM techniques from a damaged Gearbox Reliability 

Collaborative test gearbox. The purpose of the study is to demonstrate the 

capabilities and limitations of each technique. In order to provide the audience a 

relatively complete picture, a brief discussion of CM system implementation is 

provided first. The dynamometer test setup, along with some failure information on 

the damaged gearbox obtained during its disassembly, is presented next, followed 

by the CM results obtained during the retest of the damaged gearbox. For 

comparison and discussion purposes, earlier results obtained from a test gearbox 

under healthy condition will also be included. Finally, the paper will conclude with 

some observations obtained during the tests.

BACKGROUND 

Wind energy is presently the fastest growing renewable energy source in the 

world. However, the industry still experiences premature turbine component 

failures, which lead to increases in the cost of energy. With the installation of 

offshore turbines and the increase in turbine size, these failures become extremely

costly. As a result, there is a need for the industry to increase turbine reliability and 

reduce downtime. Among the various subsystems comprising a utility-scale wind 

turbine, the gearbox has been shown to cause the longest downtime and is the most 

costly to maintain throughout a turbine’s 20-year design life [1

To understand the causes of premature failures in wind turbine gearboxes and 

propose possible improvements to the wind industry, the National Renewable 

Energy Laboratory (NREL) has initiated a consortium called the Gearbox 

Reliability Collaborative (GRC). The GRC brings together different parties 

involved in the gearbox design, manufacture, and maintenance with the common 

goal of improving the reliability and extending the lifetime of wind turbine 

gearboxes.

].
2

Condition Monitoring (CM) is one research area under the GRC. It can help the 

industry achieve the goal of improved turbine uptime by enabling better operation 

and maintenance practices. In a broad sense, CM of a utility-scale wind turbine can 

target almost all of its major subsystems, including blades, nacelle, drivetrain, 

tower, and foundation. The CM discussed in this paper is narrower, however, and 

focuses on the monitoring of the wind turbine drivetrain, i.e. the main bearing, 

gearbox, and generator for a geared turbine.

Various tests have been conducted under the GRC. During the field test of one 

GRC gearbox, it experienced two unexpected oil losses that led to damage to its 

internal components. The damaged gearbox provided a unique opportunity to 

evaluate different CM technologies by retesting of this gearbox in the NREL 2.5 

MW dynamometer. Various CM techniques were applied during the retest; their 

results are the focus of this paper.

This study is a continuation of the paper, “Investigation of Various Wind 

Turbine Drivetrain Condition Monitoring Techniques” [2

CONDITION MONITORING SYSTEM IMPLEMENTATION

], presented at the 7th

International Workshop on Structural Health Monitoring (IWSHM) held in 2009. 

The main objective of the study discussed in this paper is to demonstrate the 

capabilities and limitations of each CM technique. First, a brief discussion of CM 

system implementation is provided. Then the dynamometer test setup and some 

failure information on the damaged gearbox obtained during its disassembly is 

presented. Next, the CM results obtained during the retest of the damaged gearbox

are described. For comparison and discussion purposes, earlier results obtained 

from a test gearbox under healthy conditions are included. The paper concludes

with observations obtained during the GRC tests.

The CM system was mainly implemented using an integrated approach by 

working with several commercial equipment suppliers. It took an integrated 

approach because no single technique can provide the comprehensive and reliable 

solutions needed by the industry. Four CM techniques were initially applied: 

Acoustic Emission (AE) (specifically, stress wave); vibration; offline (or kidney 

loop) real-time lubricant CM; and offline oil sample analysis. As the GRC tests 

progressed, inline (or main loop) real-time lubricant CM and electric signaturebased techniques were added.

Figure 1 illustrates the CM system setup, excluding the offline oil sample 

analysis, for the retest of the damaged gearbox in the NREL 2.5-MW dynamometer 

test facility. This setup is designed so that whenever a wind turbine drivetrain 

component starts to fail, one or several of these monitoring technologies will detect 

the change and provide reliable diagnostics. The stress wave represents the AEbased technique, which covers the frequency range above 20 kilohertz (kHz). Inline 

particle counts are the number of ferrous particles seen in the main lubrication loop 

with a size greater than 350 micrometers (μm). The offline oil condition sensor 

measures total ferrous debris in parts per million (ppm); relative humidity as a

percentage; and oil quality (changing with the level of such contaminants such as 

soot, oxidation products, glycol, water, etc.) in customized scale [2]. The offline 

International Organization for Standardization (ISO) cleanliness level indicates the 

amount of particles seen in 1 milliliter (mL) of monitored lubricant that can be 
3

classified into three size bins: ≥4 μm, ≥6 μm, and ≥14 μm [ 3]. Offline particle 

counts reflect the amount of particles detected in the kidney loop, and the total 

counts can be divided into five size bins for both ferrous and non-ferrous particles. 

Vibration uses accelerometers that target frequency ranges from close to direct 

current (DC) up to 10 kHz. Electric signature measures three-phase electric currents 

and voltages fed by the test turbine generator to the grid.

Figure 1. The GRC CM system implementation.

TEST SETUP

Different GRC dynamometer tests have a similar setup with only minor 

variations. Figure 2 shows one dynamometer test configuration with the test turbine 

installed. Figure 3 illustrates the test gearbox internal configuration.

Figure 4 shows one type of damage that occurred to the test gearbox. The 

damaged component is the high-speed stage gear located on the Intermediate-Speed 

Shaft (ISS). The gear suffered from severe scuffing. The main root cause of this 

damage is lubricant starvation and overheating contacting gear teeth surfaces. The 

high-speed stage pinion has 22 teeth, and the gear has 88 teeth. At 1800 revolutions 

per minute (rpm), this gear set has a meshing frequency of 660 Hz. The detection of 

this damage will be used in the following section to illustrate the results obtained by 

various CM techniques.
4

Gearbox

Main 

Bearing

Non-Torque 

Loading System 

Jack 

Shaft

Dynamometer 

Gearbox

Test Turbine 

Controller

Brake Low-Speed 

Shaft 

Bed 

Plate

High-Speed 

Shaft

Generator 

Transformer

Figure 2. Dynamometer setup with a test turbine installed. (NREL/PIX16913)

Annulus

Planet

Sun

Gear

Gear Pinion

Pinion

Low-Speed

Stage

High-Speed 

Stage

Intermediate-Speed

Stage

Low-Speed

Shaft

Intermediate-Speed 

Shaft

High-Speed 

Shaft 

Planet 

Carrier

Figure 3. Internal configuration of the test gearbox. Figure 4. Damage to high-speed stage gear.

RESULTS FROM VARIOUS CM TECHNIQUES

The GRC project is a work in progress. Various tests have been conducted over 

several years. This section of the paper presents some results obtained during the 

retest of the damaged gearbox in the NREL dynamometer. Selected earlier results 

obtained from a healthy gearbox are also presented for comparison and discussion.

The first set of results (Figure 5 [4]) was obtained by the vibration-based CM 

technique. They are spectra of vibration data collected by one accelerometer 

mounted to the ISS on the back cover of the test gearbox. Figure 5 (a) shows 

measurements from the healthy gearbox, and Figure 5 (b) 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 with a 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 2nd harmonic are labeled in the figures. By 

comparing Figure 5 (b) to 5 (a), it is clear that the damaged gearbox has more 

frequency components and an elevated amplitude for GMF sidebands. For 
5

illustration purpose, the ten sideband frequencies (five below and five above) the 

2nd harmonic of the high-speed stage GMF are labeled in Figure 5 (b). Such a 

spectrum pattern as illustrated in Figure 5 (b) typically represents abnormal gear set 

behaviors. Since the sideband frequencies are clearly around 39,600 cpm, it is easy 

to tell that the problematic gear set is on the high-speed stage. In addition, the 

amplitude of the fundamental high-speed stage GMF has increased from 0.0325 g 

for the healthy gearbox to 0.0875 g for 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 from the healthy gearbox. This 

is a result of both increased amplitude of those frequencies already contained by the 

healthy gearbox and the addition of 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.

The second set of results (Figure 6) was obtained by the AE (specifically, stress 

wave)-based CM technique. The stress wave sensor was mounted axially in the 

middle of all 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 

[5

5

]. 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 [ ]—as shown in Figure 6, is generated. In healthy machines, the 

distribution should be a narrow bell shape and located at the lower end of the 

voltage scale, as illustrated by Figure 6 (a). In abnormal machines, the distribution

(a) Healthy gearbox
6

(b) Damaged gearbox

Figure 5. Readings from vibration-based CM technique.

should be much broader and shifted to the right on the amplitude scale, as 

illustrated by Figure 6 (b). Figure 6 (a) implies that the monitored gear sets were 

healthy and Figure 6 (b) implies that the monitored gear sets were problematic. In 

order to identify whether the damaged gear set is on the high-speed or the 

intermediate-speed stage, it is necessary to get some frequency domain information. 

Because such results are expected to be similar to those obtained from the 

vibration-based technique, they are not presented.

The third set of results (Figure 7) was obtained from the oil-based CM 

technique; specifically, these results were obtained from the inline oil particle 

counting sensor. The counts represent the number of ferrous particles with a size 

greater than 350 μm and detected by the sensor over a certain period of time. As 

shown in Figure 7, the particle generation rate reached about 70 particles per hour

(a) Healthy gearbox
7

(b) Damaged gearbox

Figure 6. Readings from AE-based CM technique.

on 9/16, when the damaged gearbox was tested. Comparatively, about 11 particles 

were generated over a period of 4 hours from a healthy gearbox [6]. It is therefore 

reasonable to conclude based on the oil particle counting results that the retested 

gearbox has high particle-generation rates. Some damage has occurred to its 

internal components, and further investigation is recommended. However, due to 

the lack of frequency information provided by either the vibration or AE-based 

technique, it is difficult for real-time oil CM to determine where the wear was 

generated.

Figure 7. Readings from oil-based CM technique.

Additional investigation within the realm of oil CM tecniques was conducted by 

sending both oil samples taken during the disassembly of the damaged test gearbox 

and filter cloth used to drain its oil to a dedicated oil analysis laboratory. One test of 

the filter cloth conducted using a scanning electron microscope (SEM) analysis 

indicated that the major particulate constituents were steel, iron oxide, brass, and 

zinc. These elements most likely came from gears or bearings [7

If the oil analysis results are considered as the fourth set of reported results, the 

fifth set of results should be from the electric signature-based technique. However, 

preliminary analysis of the electric currents and voltages collected during the retest 

of the damaged gearbox did not show any indication of gearbox damage. One 

possible reason is that the damage on the test gearbox is not severe enough for the 

electric signature-based technique to detect. Therefore, no detailed results are 

reported here.

].
8

CONCLUSIONS AND FUTURE WORK

This paper reports results obtained using various CM techniques based on the 

test of a damaged wind turbine gearbox. It is clear that each technique has its 

strengths and limitations. The results presented here demonstrate that vibration and 

AE-based techniques can diagnose the test gearbox to have abnormal behavior and 

pinpoint the damage location using some analysis in the frequency domain.

However, they cannot provide information on lubricant condition and identify 

possible root causes for such damage. The real-time oil CM technique, on the other

hand, showed increased particle generation rates when the gearbox with damaged 

components was tested. Therefore, it can be used to identify possible damage to 

gearbox components. The offline oil sample analysis could be used to identify 

possible sources of wear particles and support root causes analysis. However, the 

oil-based CM technique cannot tell the stage of the test gearbox at which the 

damage occurred. Based on the damage level experienced by the test gearbox, the 

electric signature-based technique appeared less effective than the vibration, AE, or 

oil-based monitoring techniques for gearbox damage diagnostics. These results 

demonstrate the necessity to integrate various techniques when conducting wind 

turbine drivetrain CM. Further research will be conducted on data fusion trying to 

increase CM diagnostics reliability and its cost effectiveness.

ACKNOWLEDGEMENTS

The author would like to acknowledge the Wind and Water Power Program, 

Office of Energy Efficiency and Renewable Energy of the U.S. Department of 

Energy, for its support of this work. The author also thanks the CM partners for 

their contributions to the GRC CM research.

REFERENCES

1. Sheng, S. and P. Veers. 2011. “Wind Turbine Drivetrain Condition Monitoring—An 

Overview,” in Proceedings, Machinery Failure Prevention Technology (MFPT): The Applied 

Systems Health Management Conference 2011, May 10-12.

2. Sheng, S., S. Butterfield, and F. Oyague. 2009. “Investigation of Various Wind Turbine 

Drivetrain Condition Monitoring Techniques,” in Proceedings, 7th International Workshop on 

Structural Health Monitoring 2009, September 9-11.

3. International Organization for Standardization (ISO). 1999. “Hydraulic Fluid Power—Fluids—

Method for Coding the Level of Contamination by Solid Particles,” ISO 4406:1999(E). Geneva: 

ISO.

4. Ziegler, G. 2011. “General Observations SKF Condition Monitoring System Update,” presented 

at the NREL GRC All Member Meeting, February 16-17.

5. Board, D. B. 2003. “Stress Wave Analysis Provides Early Detection of Lubrication Problems,” 

Practicing Oil Analysis magazine, July.

6. Dempsey, P. and S. Sheng. 2011. “Investigation of Data Fusion Applied to Health Monitoring 

of Wind Turbine Drivetrain Components,” presented at the 2011 Wind Power Conference, May 

22-25.

7. Roylance, B. and T. Hunt. 1999. The Wear Debris Analysis Handbook. Oxford, UK: Coxmoor.