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

Sideband Algorithm for 

Automatic Wind Turbine 

Gearbox Fault Detection and 

Diagnosis

Preprint

D. Zappalà, P. Tavner, and C. Crabtree

Durham University

S. Sheng

National Renewable Energy Laboratory

To be presented at the European Wind Energy Association 2013 

Annual Event

Vienna, Austria

February 4-7, 2013

Conference Paper

NREL/CP-5000-57395

January 2013
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1 

Sideband Algorithm for Automatic Wind Turbine Gearbox 

Fault Detection and Diagnosis

Donatella Zappalà Peter J. Tavner

School of Engineering and Computing Sciences School of Engineering and Computing Sciences

Durham University, UK Durham University, UK

donatella.zappala@durham.ac.uk peter.tavner@durham.ac.uk

Christopher J. Crabtree Shuangwen Sheng

School of Engineering and Computing Sciences National Renewable Energy Laboratory (NREL)

Durham University, UK Golden, Colorado, United States

c.j.crabtree@durham.ac.uk shuangwen.sheng@nrel.gov

Abstract

Improving the availability of wind turbines (WT) is 

critical to minimise the cost of wind energy, 

especially for offshore installations. Because 

gearbox downtime has a significant impact on WT 

availabilities, the development of reliable and costeffective gearbox condition monitoring systems 

(CMSs) is of great concern to the wind industry. 

Timely detection and diagnosis of developing gear 

defects within a gearbox is an essential part of 

minimizing unplanned downtime of wind turbines.

Monitoring signals from WT gearboxes are highly 

non-stationary because turbine load and speed vary 

continuously with time. Manual handling of large 

amounts of monitoring data is time-consuming and 

costly, and is one of the main limitations of most 

current CMSs. Therefore, automated algorithms are 

required.

This paper presents a fault detection algorithm for 

incorporation into a commercial CMS for automatic

gear fault detection and diagnosis. Based on the 

experimental evidence from the Durham condition 

monitoring test rig, a gear condition indicator has 

been proposed to evaluate the gear damage during 

non-stationary load and speed operating conditions.

The algorithm allows the assessment of gear fault

severity by tracking progressive tooth gear damage 

during variable speed and load operating conditions 

of the test rig.

The performance of the proposed technique has 

then been successfully tested on signals from a field 

test of a full-size wind turbine gearbox that has 

sustained gear damage.

Results show that the proposed technique proves 

efficient and reliable for detecting gear damage. 

Once implemented into WT CMSs, this algorithm 

can automate data interpretation, reducing the 

quantity of information that WT operators must 

handle. 

Keywords: wind turbine, gearbox, condition

monitoring, gear tooth fault, vibration analysis

1 Introduction

Achieving high WT availability is paramount to 

providing affordable and cost-effective wind energy, 

especially with the current growth in turbine size and 

capacity, and the installation in increasingly remote 

and less accessible offshore locations. To make 

wind power more competitive compared to 

conventional sources, it is necessary to reduce 

turbine downtime and increase reliability [1]. The 

development of reliable and cost-effective online 

condition monitoring (CM) techniques with automatic 

damage detection and diagnosis of WT components 

can help achieve predictive, condition-based 

maintenance. This will reduce the operation and 

maintenance (O&M) costs and thereby the cost of 

wind energy. Gearbox faults, with high replacement 

costs, complex repair procedures, and revenue loss 

caused by long downtime [2], are widely considered 

a leading issue for WT drive train CM.

In this paper, the effect of gear tooth fault severity on 

the gearbox vibration signature is investigated using 
2 

experimental results obtained on the Wind Turbine 

Condition Monitoring Test Rig (WTCMTR) at 

Durham University. A frequency tracking algorithm 

that automatically detects and diagnoses gear tooth 

faults is proposed and discussed. The performance 

of the proposed technique is then tested using 

vibration signals from a real 750-kW wind turbine 

gearbox that has sustained gear damage from its 

field test.

2 Wind Turbine Gearbox

Condition Monitoring

Among the various wind turbine components, the 

gearbox has been shown to cause the longest 

downtime [2,3] and is the most costly to maintain 

throughout a turbine’s 20-year-plus design life [4]. 

Typical WT gearbox failure modes are gear tooth 

damage and bearing faults [5]. A recent study [6]

has shown that the gearbox alone could be 

responsible for up to one-third of all lost onshore WT 

availability.

This problem is emphasised offshore where harsh 

weather and sea conditions could prevent 

maintenance or component replacement for long 

periods of time. Few reliability data are still publicly 

available for offshore WTs. However, 3 years of 

available data from Egmond aan Zee wind farm in 

the Netherlands [7] show how the gearbox downtime 

caused 55% of the total wind farm downtime [8]. 

The main WT Operator concerns about gearbox 

reliability, particularly offshore, are: high replacement 

costs following a failure; complex repair procedures 

that incur high logistics costs and require favourable 

weather conditions; and high revenue losses caused 

by long downtime between failures and repair 

completion [5]. Consequently, the gearbox has 

become an essential part of the current commercial 

available WT CMSs. Timely and reliable detection 

and diagnosis of developing gear defects within a 

gearbox is an essential part of minimizing unplanned 

downtime of wind turbines.

The most popular CM approach for the gearbox is 

vibration monitoring using Fourier transform analysis

[9]. WTs are variable load and speed systems 

operating under highly dynamic conditions. This 

results in CM signals that vary significantly in both 

magnitude and spectral content. The frequent false 

alarms and the costly specialist knowledge that is 

required for manual interpretation of the complex 

vibration data have discouraged WT Operators from 

making wider use of CMS. This happens despite the 

fact that these systems are fitted to the majority of 

large WTs (>1.5 MW) in Europe [10]. Moreover, with 

the growth of the WT population, especially offshore, 

the manual examination and comparison of the CM 

data will be impractical unless a simplified

monitoring process is introduced.

Current efforts in the wind CM industry are aimed at

automating data interpretation and improving the 

accuracy and reliability of diagnostic decisions, 

especially in the light of impending large-scale, 

offshore wind-farm generation. This study attempts 

to target this research area by experimentally 

defining an algorithm that could be incorporated into

current CMS for automatic gear fault detection and 

diagnosis. This algorithm can reduce the quantity of 

information that the WT operators must handle, 

providing improved detection and timely decisionmaking capabilities.

3 WT Condition Monitoring Test 

Rig

Experimental research was performed on a 30-kW 

WTCMTR at Durham University, which has been 

designed to act as a model for a WT drive train. 

Figure 1 provides a schematic of the WTCMTR and 

Figure 2 is a photo of the test stand, which shows 

the main components and instrumentation and 

control systems.

The rig features a 54-kW DC motor driving a 3-

phase, 4-pole, 30-kW wound rotor induction 

generator (WRIG) through a 5:1 two-stage helicalgear parallel shaft gearbox. The WRIG has external 

variable resistors connected to the rotor circuit that 

allow a super-synchronous speed variation of 100 

rev/min, 1500-1600 rev/min, with a corresponding 

generator maximum power of 3.6 kW. The DC 

motor was driven at constant and wind-like variable 

speed conditions to cover the full allowed speed 

range. 

Variable speed machine testing was performed 

using driving data derived from a 2-MW WT model
3 

[11]. Vibration data from an accelerometer located 

on the gearbox high speed (HS) end have been 

processed using an SKF WindCon unit 3.0, a 

commercial CMS currently used on full-size

operational WTs, which produce Fast Fourier 

Transform (FFT) spectra. The sensor used is a 

piezoelectric accelerometer with integral electronics 

and a sensitivity of 500 mV/g. WindCon’s built-in 

diagnostic system has been used to assist with the 

analysis of the spectra by tracking the machine 

component-specific, speed-dependent fault 

frequencies and their harmonics.

Figure 1: Schematic diagram of the Durham WTCMTR [11].

Figure 2: Durham WTCMTR instrumentation and 

control systems [11]. 

4 Damaged Gearbox Investigation

Experiments were conducted to investigate the 

progression of a tooth defect on a high speed shaft 

(HSS) pinion, which was introduced into the 

WTCMTR at variable-speed and generator load. The 

behaviour of a healthy pinion and of four faults of 

increasing severity were investigated by introducing 

progressive damage to the leading contact edge of 

one tooth of the gearbox pinion. Figure 3(a) shows 

the healthy pinion, Figures 3(b), (c), and (d) show 

early stages of tooth wear, while Figure 3(e) depicts 

the entire tooth missing.

The most important components in gear vibration 

spectra are the tooth meshing frequency and its 

harmonics, together with sidebands caused by

modulation phenomena. The increment in the 

number and amplitude of such sidebands may 

indicate a fault condition. Moreover, the spacing of 

the sidebands is related to their source [12]. 
4 

Typically, the more damage that occurs, the more 

energy there is in the sidebands [13]. 

Previous work of the authors on the WTCMTR [14]

has shown that the presence of a faulty pinion can 

be clearly seen, from the FFT order spectrum, in the 

HS stage meshing frequency second harmonic

2 X fmesh,HS, which is heavily modulated by the HSS 

speed. Furthermore, the gear mesh centre 

harmonic, surrounded by the sidebands, denotes 

which gear mesh the damaged gear is passing 

through. These two pieces of information indicate 

that the damaged component is passing through the 

HS stage gear mesh and is mounted on the HSS 

shaft.

Based on the experimental evidence, a gear 

condition indicator, the Sideband Power Factor 

(SBPF) algorithm, was proposed [14] to evaluate the 

gear damage during the WT non-stationary load and 

speed operating conditions. The SBPF algorithm

sums the Power Spectrum amplitudes of the HSstage meshing frequency second harmonic and its 

first 5 sideband peaks on each side. It has been 

calculated using 

SBPF = PSA�2 X fmesh,HS� + � PSA(SBi) (1)

+5

i=−5

where PSA�2 X fmesh,HS� and PSA(SBi) , with 

i = ±1, ±2 , ±3, ±4, ±5 , are the power spectrum 

amplitudes of the 2 X fmesh,HS harmonic, and of its 

first 5 sidebands spaced at the HSS rotational 

speed, respectively.

The proposed algorithm facilitates the monitoring 

analysis, reducing each FFT spectrum to only one 

parameter for each data acquisition. By automating 

the condition monitoring of the gears, SBPF reduces

the quantity of vibration information that WT 

operators must handle, providing improved detection 

and timely decision-making capabilities. SBPF can 

be monitored over time, trended, and compared to 

one or more predetermined threshold levels to 

provide warnings and alarms to operators.

5 Fault Detection and Diagnosis 

Based on SBPF Algorithm

The influence of the fault severity and the variable 

load operating conditions on the SBPF values has 

been investigated by performing tests on the 

WTCMTR at a load up to 3.6 kW.

The resulting SBPF values are shown in Figure 4 

against the load, expressed as a percentage of the 

maximum generator output in this condition, for HSS 

pinion healthy conditions, for early stages of tooth 

wearing, and for a missing tooth.

The results show that the SBPF magnitude is 

proportional to the magnitude of the gear fault level. 

This is because as damage develops on a gear 

tooth passing through the gear mesh, the sidebands 

increase in amplitude, resulting in larger SBPF 

values. The trend of the obtained SBPF values can 

be fitted by an exponential curve, relating vibration 

spectra power increase with machine load.

In the full range of the load investigated, the SBPF 

values for the missing tooth case are higher than 

both the healthy and early tooth wear cases, 

indicating clear fault detection. The proposed 

algorithm works successfully even at the early 

stages of the tooth failure, showing a higher 

effectiveness at percentage loads above 20%.

Figure 3: HSS pinion conditions investigated during the tests: (a) healthy, early stage of tooth wear; (b) 3-mm x 

2-mm chip; (c) 5-mm x 5-mm chip; (d) 7-mm x 5-mm chip; (e) missing tooth.
5 

Figure 4: Influence of the fault severity and the variable load operating conditions on the SBPF values.

The SBPF detection sensitivity to tooth damage, 

shown in Figure 5, has been calculated by 

determining, for each load condition, the percentage 

change of the SBPF value. For each case, the SBPF 

detection sensitivity (%SBPF) between faulty and 

healthy conditions has been defined as 

%SBPF = SBPFf − SBPFh

SBPFh

∗ 100 (2)

where SBPFh and SBPFf are the SBPF values for the 

healthy and faulty cases, respectively. 

The sensitivity analysis shows that:

• The SBPF technique proves successful in the 

detection of both early and final stages of gear 

tooth damage, with average detection 

sensitivities of 100% and 320%, respectively.

• The missing tooth fault is marginally easier to 

discriminate at lower powers, while the minor 

faults are slightly easier to discriminate at higher 

powers.

Experimental work on the low-power Durham 

WTCMTR has allowed the implementation of 

repeated fault-like conditions under controlled 

conditions. The developed SBPF methodology, 

based on the analysis of the dynamics of the gears, 

can easily be scalable to higher WT power levels. 

However, this would probably imply an increase in 

the spectral background noise because of the higher 

complexity of WT drive trains compared to the smallscale WTCMTR. 

For gearbox parallel stages, SBPF is easily 

applicable to the harmonics of each fundamental 

gear mesh frequency using both gear and pinion 

sidebands. For planetary stages, the analysis of the 

sideband patterns could be more complicated

because of the low mechanical transmissibility from 

gear components and the multiple contact points 

between each planet gear meshing and the sun and 

ring gear. 
6 

Figure 5: SBPF detection sensitivity to early stages of tooth wear and tooth missing for the experimentally 

investigated WTCMTR power loads. 

6 NREL Wind Turbine Gearbox 

Condition Monitoring Round 

Robin Project 

To validate the performance and reliability of the 

proposed SBPF algorithm on a full-size gearbox, the 

algorithm has been tested on data from the Wind 

Turbine Gearbox Condition Monitoring Round Robin 

project [15]. Vibration data collected from two 

identical 750-kW wind turbine gearboxes that were

tested at the National Renewable Energy Laboratory 

(NREL) dynamometer test stand [16,17], shown in 

Figure 6, were used in this study. The gearboxes

have an overall ratio of 1:81.491 and feature one 

low-speed (LS) planetary stage and two parallel 

stages, and an intermediate speed (IS) and high 

speed (HS) stage, respectively. Baseline data were 

collected on the dynamometer test stand from a 

healthy test gearbox, which did not have any 

operational experience.

Figure 6: NREL dynamometer test stand with 750-kW gearbox article installed. 

Photo by Lee Jay Fingersh / NREL 16913. 
7 

Data then were collected from the dynamometer 

retest of an identical gearbox after its internal 

components had sustained damage from its field 

test. This gearbox first finished a run-in in the NREL 

dynamometer and was then sent to a nearby wind 

farm for a field test. The test gearbox was installed 

on a three-blade, stall-regulated, upwind wind

turbine with a rated power of 750 kW and a rated 

wind speed of 16 m/s. In the field, two oil loss events 

occurred and led to some damage to gears and 

bearings inside the test gearbox. The gearbox was 

then removed from the field and retested under

controlled conditions in the NREL test stand. During 

the retest, various condition monitoring data were 

collected, including measurements of vibration and 

oil debris. Once the dynamometer retest was 

completed, the gearbox was disassembled and a 

detailed failure analysis was conducted [18]. Severe

scuffing of the HS shaft gear set was one of 12 

instances of damage found during the failure 

analysis. Figure 7 shows the damaged HSS pinion.

Figure 7: Pinion damage on the 750-kW gearbox 

HSS. Photo from GEARTECH, NREL / 19743.

The task of this study was to detect and diagnose 

that damage by using the SBPF analysis of the 

vibration data collected by an accelerometer

mounted radially on the gearbox HSS. The 

accelerometer was an integrated-circuit 

piezoelectric-type with a sensitivity of 100 mV/g. 

The available dataset refers to an HSS speed of 

1800 rev/min and to 50% of rated power, which is 

the highest test load applied in order to reduce the 

chances of a catastrophic gearbox failure. It

contains:

• For the healthy gearbox: 1 single FFT spectrum 

collected by a commercial condition monitoring 

system at 5 kHz for a duration of 1.6 sec

• For the faulty gearbox: 40-kHz raw vibration 

data collected continuously for 10 minutes. 

The dataset presented some challenges to deriving

a system baseline for sideband amplitude 

comparison purposes. This was overcome by 

windowing data from the faulty gearbox through a 

1.6s time window and then processing the data 

using the built-in Fast Fourier Transform algorithm in 

MATLAB. The resulting 375-FFT spectra have then

been consistently compared against the available

healthy spectrum, presenting the same frequency 

resolution. Figure 8 shows an example of the zoomin view of the healthy and faulty spectra around the 

HS stage meshing frequency second harmonic, 

2 X fmesh,HS, given by

2 X fmesh,HS = 1323 Hz (3)

When comparing the degraded gearbox to the 

nominal baseline healthy gearbox, the increase in 

energy content of the 2 X fmesh,HS harmonic and its 

sidebands can be clearly seen. In the faulty 

spectrum, the HS stage meshing frequency second 

harmonic is heavily modulated by the HSS rotational 

speed, 30 Hz. The sideband spacing indicates 

severe damage in the HS pinion [14].

To quantify these observations from the vibration

data, SBPF values were extracted from the baseline 

spectrum and the 375 degraded gearbox spectra. 

The results are shown in Figure 9. 

The SBPF magnitude is much larger for the 

degraded gearbox compared to the baseline

gearbox, representing an average value of 0.025

(gP)2

. From the SBPF methodology, there is a 

strong indication that there is damage on the highspeed shaft pinion. These results, on a full-size 750-

kW gearbox, provide further credibility to the SBPF 

algorithm, already proven on the 30-kW WTCMTR, 

for timely detection and diagnosis of gear damage.
8 

Figure 8: FFT vibration spectra for the healthy and the faulty 750-kW gearboxes in the [1140-1500] Hz 

frequency bandwidth.

Figure 9: SBPF comparison between baseline and faulty 750-kW gearboxes. 

The SBPF detection sensitivity to tooth damage,

shown in Figure 10, has been defined in equation (2) 

by considering the percentage change in the SBPF 

values between faulty and healthy cases. Although

the gearbox damage was more complex than in a 

typical operational wind turbine [15] and the dataset 

provided refers to only one speed and load 

operational condition, the SBPF detection and 

diagnostics technique proves successful in the 

detection of HS pinion damage, with an average

detection sensitivity of 1251%.

Because the analysed dataset contains multiple 

gearbox progressed faults, it is believed that in 

actual practice the SBPF diagnostic performance 

could be improved when deployed in the field, 

bearing in mind the smaller number of faults usually 

presented during the early stages of gearbox fault 

evolution. 

1140 1170 1200 1230 1260 1290 1320 1350 1380 1410 1440 1470 1500 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Frequency [Hz]

Amplitude [gP]

Healthy Gearbox

Faulty Gearbox
9 

Figure 10: SBPF detection sensitivity to damaged HS shaft pinion for 750-kW gearbox NREL dataset.

7 Conclusions

In conclusion, this work shows that:

• The SBPF algorithm proved effective in 

detecting the presence of gear damage within a 

30-kW Test Rig gearbox, i.e., damage location, 

and in identifying the precise damaged gear, i.e., 

damage diagnosis, with a detection sensitivity of 

300% to 350%. 

• The SBPF has successfully allowed the 

assessment of gear fault severity on the Test 

Rig by tracking progressive tooth damage, from 

the early stages of development, during variable 

speed and load conditions.

• The experimentally defined SBPF technique has 

also been successfully tested against vibration 

data from an NREL 750-kW WT gearbox, which 

had experienced severe high-speed shaft gear 

set scuffing, with a detection sensitivity of 

1251%. 

• The proposed methodology is relatively simple 

to implement into a commercial WT CMS for 

automatic gear fault detection and diagnosis.

• The generation of SBPF trends from the 

vibration spectra and the definition of magnitude 

thresholds for the fault severity levels could 

indicate to a WT operator when a maintenance 

action needs to be performed.

• SBPF can be easily adapted to detect gear 

damage on all the WT gearbox parallel stages, 

while its applicability to planetary stages still 

requires more investigation. 

• Compared to the conventional FFT approach 

used in current commercial vibration-based 

CMSs, requiring time-consuming visual spectra 

analysis, the SBPF approach enables automatic

detection and diagnosis of gear faults with low 

risk of false alarms. This will lead to increased 

accuracy of wind turbine drive-train vibrationbased condition monitoring.

Acknowledgements

This work was funded as part of the UK EPSRC 

Supergen Wind Energy Technologies programme, 

EP/H018662/1. The authors thank NREL for 

providing the vibration data used for the validation of 

the SBPF algorithm.

References

[1] Sheng, S. and Veers, P., "Wind Turbine 

Drivetrain Condition Monitoring - An 

Overview", Proceedings of Machinery Failure 

Prevention Technology (MFPT): The Applied 

Systems Health Management Conference

2011, May 10-12.
10

[2] Faulstich, S., Hahn, B. and Tavner, P. J., 

"Wind Turbine Downtime and its Importance 

for Offshore Deployment", Wind Energy, Vol. 

14, No. 3, pp. 327-337, April 2011.

[3] Tavner, P. J., Faulstich, S., Hahn, B. and van 

Bussel, G. J. W., "Reliability & Availability of 

Wind Turbine Electrical & Electronic 

Components", EPE Journal, Vol. 20, No. 4, 

pp. 45-50, December 2010.

[4] Sheng, S., "Investigation of Various Condition 

Monitoring Techniques Based on a Damaged 

Wind Turbine Gearbox", Proceedings of the

8th International workshop on Structural 

Health Monitoring, Condition-based 

Maintenance and Intelligent Structures, 

Stanford, California September 13-15, 2011.

[5] Feng, Y., Qiu, Y., Crabtree, C. J., Long, H.

and Tavner P. J., "Monitoring wind turbine 

gearboxes", Wind Energy, Article first 

published online: 16 JUL 2012, DOI: 

10.1002/we.1521. 

[6] Gray, C. and Watson, S., "Physics of failure 

approach to wind turbine condition based 

maintenance", Wind Energy, Vol. 13, No. 5, 

pp. 395-405, July 2010.

[7] Shell NoordzeeWind, Egmond aan Zee 

Operations Reports 2007, 2008, 2009, 

available at www.noordzeewind.nl, last 

accessed 10th August 2012. 

[8] Crabtree, C. J., "Operational and Reliability 

Analysis of Offshore Wind Farms", 

Proceedings European Wind Energy 

Conference, Copenhagen, Denmark, April 

2012.

[9] Crabtree, C. J. and Zappalà, D., Survey of 

Commercially Available Condition Monitoring 

Systems for Wind Turbines, Supergen Wind 

Energy Technologies Consortium, UK, 2011, 

available at www.supergen-wind.org.uk, last 

accessed 2nd September 2012. 

[10] Yang, W., Tavner, P. J., Crabtree, C. J., Feng, 

Y. and Qiu, Y., "Wind turbine condition 

monitoring: technical and commercial 

challenges", Wind Energy, Article first 

published online: 1 AUG 2012, DOI: 

10.1002/we.1508.

[11] Crabtree, C. J., Condition Monitoring 

Techniques for Wind Turbines, PhD Thesis, 

Durham University, UK, 2011.

[12] Randall, R. B., "A new method of modelling 

gear faults", Journal of Mechanical Design, 

Vol. 104, pp. 259-267, 1982. 

[13] Goldman, S., Vibration Spectrum Analysis: A 

Practical Approach, 2nd Edition, Industrial 

Press, Inc., pp. 87-90, 1999. 

[14] Zappalà, D., Tavner, P. J. and Crabtree, C. J., 

"Gear Fault Detection Automation Using 

WindCon Frequency Tracking", Proceedings 

European Wind Energy Conference, 

Copenhagen, Denmark, April 2012. 

[15] Sheng, S., "Wind Turbine Gearbox Condition 

Monitoring Round Robin Study - Vibration 

Analysis", NREL Report No. TP-5000-54530, 

July 2012, available at 

http://www.nrel.gov/publications/, last 

accessed 5th September 2012.

[16] Musial, W. and McNiff, B. “Wind Turbine 

Testing in the NREL Dynamometer Test Bed”, 

Proceedings American Wind Energy 

Association’s WindPower 2000 Conference,

Palm Springs, CA, April 30-May 4, 2000. 

NREL/CP-500-28411. Golden, CO: National 

Renewable Energy Laboratory, June 2000, 

available at 

http://www.nrel.gov/docs/fy00osti/28411.pdf, 

last accessed 26th November 2012.

[17] National Renewable Energy Laboratory, 

Dynamometer Testing (Fact Sheet). 

NREL/FS-5000-45649. Golden, CO, available 

at

http://www.nrel.gov/docs/fy11osti/45649.pdf, 

last accessed 14th November 2012.

[18] Errichello, R. and Muller, J., "Gearbox 

Reliability Collaborative Gearbox 1 Failure 

Analysis Report", NREL/SR-5000-530262. 

Work performed by Geartech, Townsend, 

Montana. Golden, CO: National Renewable 

Energy Laboratory, February 2012, available 

at 

http://www.nrel.gov/docs/fy12osti/53062.pdf, 

last accessed 15th September 2012.