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Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5000-76286

April 2020

Gaining Insights in Loading Events for 

Wind Turbine Drivetrain Prognostics

Preprint

Pieter-Jan Daems,

1 Yi Guo,2 Shawn Sheng,2 C. Peeters,1

P. Guillaume,1 and J. Helsen1

1 Vrije Universiteit Brussel

2 National Renewable Energy Laboratory

Presented at the Turbomachinery Technical Conference & Exposition

September 21 – 25, 2020
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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

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Conference Paper

NREL/CP-5000-76286

April 2020

Gaining Insights in Loading Events for 

Wind Turbine Drivetrain Prognostics

Preprint

Pieter-Jan Daems,

1 Yi Guo,2 Shawn Sheng,2 C. Peeters,1

P. Guillaume,1 and J. Helsen1

1 Vrije Universiteit Brussel

2 National Renewable Energy Laboratory

Suggested Citation

Daems, Pieter-Jan; Yi Guo, Shawn Sheng, C. Peeters, P. Guillaume, and J. Helsen. 

2020. Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics: 

Preprint. Golden, CO: National Renewable Energy Laboratory. NREL/CP-5000-76286. 

https://www.nrel.gov/docs/fy20osti/76286.pdf
NOTICE

This work was authored [in part] by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy 

Technologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

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Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
GAINING INSIGHTS IN LOADING EVENTS FOR WIND TURBINE DRIVETRAIN

PROGNOSTICS

Pieter-Jan Daems Y. Guo C. Peeters

Department of Mechanical Engineering S. Sheng P. Guillaume

Vrije Universiteit Brussel National Renewable Energy Laboratory J. Helsen

Pleinlaan 2, 1050 Brussel (Belgium) Golden, Colorado Department of Mechanical Engineering

Email: pieter-jan.daems@vub.be Vrije Universiteit Brussel

Pleinlaan 2, 1050 Brussel (Belgium)

ABSTRACT

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to 

accurately pinpoint the root causes of different failure modes of 

interest. An example of such a failure mode that is not yet fully 

understood is white etching cracks (WEC). This can cause the 

bearing lifetime to be reduced to 5-10% of its design value. Multiple hypotheses are available in literature concerning its cause. 

To be able to validate or disprove these hypotheses, it is essential 

to have historic high-frequency measurement data (e.g., load and 

vibration levels) available. In time, this will allow linking to the 

history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain 

events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specifc events 

that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show 

signs of damage. 

NOMENCLATURE

LVRT Low voltage ride through 

NREL National Renewable Energy Laboratory 

MW megawatt 

O&M Operations and maintenance 

SCADA Supervisory control and data acquisition 

WEC White etching crack 

INTRODUCTION

Over the last few years, the installed capacity of wind energy has increased drastically in Europe. To meet this increasing 

demand, an upscaling trend has been taking place: the turbines 

are increasing in size. It is hypothesized that by decreasing the 

number of machines needed per megawatt (MW), the operations 

and maintenance (O&M) costs of the farms can be decreased [1]. 

This trend, however, also means that the failure of a single turbine causes larger energy losses for an equivalent downtime of 

the reference turbine. In order to be able to further reduce the 

O&M costs, two aspects are essential. First, it is vital to be able 

to detect the degradation of the machine components before failure. In this way, predictive maintenance methodologies can be 

implemented, minimizing the downtime of the machine [2]. Second, it is important to be able to identify the root cause of the different failure modes, such that strategies can be found to mitigate 

the risk of these modes in future design iterations. 

This paper focuses on the reliability at the level of the wind 

turbine gearbox. According to a National Renewable Energy 

Laboratory (NREL) study [3], the most frequent gearbox failure 

modes are related to the high-speed stage bearings (48%) and the 

high-speed gears (18%). For the former, white etching cracks 

(WECs) are the most common failure mode. WECs are crack 

networks that ultimately lead to macro-pitting in bearings [4]. 

Because of WECs, the bearing lifetime can drop to 5%-10% of 

their designed value [5] and increase the O&M costs of wind 

turbine farms during their frst years of operation. 

Different hypotheses concerning the root cause of WECs ex1

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ist; however, no defnitive conclusion has been found surrounding its root causes. In [4,6], experimental studies were conducted 

to replicate WECs on a test rig and determine the infuence of 

roller slip and lubricant additives. In this paper, the events for 

which roller slip can occur are targeted. 

Wind turbines are subjected to different transient events. 

These transients can result in loading problems and unfavorable 

tribological conditions and will govern the design of the gearbox 

and bearings [7,8]. Examples of such events are the following: 

1) Run-ups at low wind speed, as these can lead to damaging 

slip and smearing inside the bearings. In [9], it was observed 

that the roller slip density reached critical levels during a 

run-up before connecting the turbine to the grid. 

2) Emergency stops and shock loads. During these events, it 

is known that the drivetrain is subject to torque reversals. 

These are introduced because of the dynamics of the gearbox 

(i.e., the frst torsional mode) and result in sudden changes 

in the orientation of the loaded bearing zone [10]. During 

these reversals, loads will be applied to unloaded rollers, 

which can be detrimental for the drivetrain health. A possible trigger for these stops are, for example, low voltage 

ride through (LVRT), events where the generator momentarily loses connection to the electrical grid. This was also 

investigated in [9] and showed high roller slip losses when 

the unloaded rollers are accelerated after reconnecting to the 

grid. 

Based on the observation that the roller slip plays a key role 

in the creation of WECs, different criteria have been proposed to 

attempt to predict the probability of WECs based on the operational history of the turbine. One is the accumulated friction energy, based on the sliding speed between the rolling element and 

the raceway and the normal load. Another similar metric is the 

power slip density. The common denominator between these two 

is that both require the ability to evaluate the loading conditions 

and the instantaneous slip. Therefore, it is vital to have detailed 

insights into the machine behavior, during both these transient 

events and the normal operating conditions. To this end, longterm high-frequency measurement data (e.g., load and vibration 

levels) should be available. In time, this will allow drawing correlations between the load history of the turbine and its failure 

record. Extensively monitoring the load and vibration levels is, 

however, unrealistic for entire feets. Therefore, instrumenting 

all turbines with a minimal set of sensors and selecting a few 

turbines within the farm to instrument extensively is proposed. 

These are the so-called feet leaders [11]. The other turbines 

within the farm are then assumed to have similar dynamic behavior during these different events of interest. By investigating 

in detail what happens at the level of the feet leaders during these 

events, it becomes possible to defne if these could be potentially 

harmful. By automatically these events on a farm-wide level and 

extrapolating the gained knowledge, it will become possible to 

obtain metrics of which turbines in the farm are most likely to 

show signs of damage. 

To move toward this methodology, two major hurdles need 

to be overcome. The frst one is related to the continuous acquisition of high-frequency measurement data at the level of the 

feet leaders. Besides the acquisition, the device also needs to be 

capable of sending the useful information to a central location, 

where information of all turbines is available. To this end, two 

different strategies are possible. One methodology is to simply 

transmit all data to the central unit. Another option is to explore 

the viability of edge computing (i.e., processing part of the 

data at the level of the turbine and only transmit these results). 

The second challenge is related to the automatic processing 

of the data. For the feet leaders, this includes having a set of 

algorithms available that can continuously process the incoming 

stream of high-frequency data, as the insights to be gained from 

the raw vibration and strain signals are often limited. Based on 

this, events of interest can be defned by the analyst. For the 

other turbines in the farm, these events should then be extracted 

automatically. In this way, statistics can be kept on how many 

events each turbine has seen, allowing correlations between 

these data and failure records in a fnal stage. 

METHODOLOGY

This paper chooses to concentrate on the aspect of automatically processing the measured data. Within this context, two data 

sources are considered: Supervisory control and data acquisition 

(SCADA) data and strain gauge data measured at the level of 

the main speed shaft of the turbine. When data are continuously 

acquired, there is a need to separate steady-state behavior and 

transient behavior. Once a study of the machine loading in the 

different operating conditions has taken place, a damage metric 

can be attributed to different events and to steady-state operation. The latter is, however, beyond the scope of the paper, as 

the chosen metric is strongly problem dependent and is a feld 

where extensive research has already taken place. Instead, the 

main goal is to present a framework where events are automatically detected and classifed on a farm-wide level. This will be 

done in two steps. 

First, data from the U.S. Department of Energy (DOE) 1.5-

MW turbine will be analyzed, as detailed instrumentation is 

available on this machine. Therefore, it can be used to gain detailed insights in what is happening for different similar machines 

for which only SCADA is available. To illustrate the developed 

algorithms, the forces at the level of the main shaft will be refected to the carrier bearings. These forces can be in a last step 

condensed into a damage metric. Moreover, different events of 

interest are automatically identifed using SCADA data of this 

turbine using a machine learning approach based on pattern mining and pattern matching. Second, one year of SCADA-1s data of 

2

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an offshore wind farm will be analyzed. Because no force instrumentation is available on this wind farm, the focus here is solely 

on the detection of the different events. However, the algorithms 

implemented for assessing the bearing forces on the DOE turbine 

remain valid, as long as adequate design information of the gearbox is available and the needed measurement instrumentation is 

in place. The NREL data set is therefore used to demonstrate the 

feasibility of the different algorithms. 

A schematic representation of the proposed methodology is 

shown in Figure 1. It can be seen that the general idea is to use the 

feet leader to build a database for the events the turbine can see 

in the different ambient conditions (e.g., wind speeds and wind 

directions). This will allow an approximation of the loading that 

takes place during these conditions. Ideally, multiple feet leaders are available to take into account wake effects throughout the 

farm. With this framework, it is possible to get an approximation of the accumulated damage for all turbines within the farm, 

allowing assessment of which machines are most likely to show 

damage. 

Load assessment

As mentioned, there is use of the measurement data of the 

DOE 1.5 -MW turbine located at NREL. Within the context of 

this paper, especially the instrumentation at the level of the main 

shaft are of interest. As the reliability of the bearings is central 

within this paper, there is the option to translate these forces to 

the bearing loads of the carriers using Equation 1. These equations can be retrieved by means of expressing the force and moment equilibrium for the system containing the main shaft and 

the carriers. The full derivation of the algorithm, as well as the 

made assumptions, can be found in [12]. The estimation of the 

tangential bearing forces ft,i requires having knowledge of several gearbox design parameters such as the tilting angle ρ, the 

mounting angle βi, the shaft mass msh, and the distances from the 

main bearing to the centers of gravity of the main shaft Lsh, carrier Lc, and planetary gear Lp, respectively. ei are the deviation 

angles from the designed circumferential position (e.g., because 

of the planet-bearing clearance). For the gearbox under study, 

these parameters have been supplied by the manufacturer. These 

forces are cyclical in time, with Ωc being the rotational frequency 

of the carrier axes. Because this force estimation requires continuous measuring of the applied moments to the drivetrain My and 

Mz and the applied torque Tin, it is required to have strain information at the level of a drivetrain shaft. The unknown forces ft,i 

are then obtained on each time instance by minimizing the error 

terms ε1, ε2, and ε3. 

FIGURE 1: BLOCK DIAGRAM SHOWING THE 

SUGGESTED METHODOLOGY. 

3 1 My Lp ε1 = mshgLsh cos(ρ) + − ∑ ft,i cos(Ωct −βi +ei) 2 Lc i=1 Lc 

3 Lp + ∑ mpgsin(Ωct +βi +ei)

2 cos(ρ) +mgcos(ρ)

i=1 Lc 

3 

ε2 = ∑ ft,idc −Tin (1) 

i=1 

3 

ε3 = − ∑ ft,isin(Ωct +βi +ei)Lp − Mz 

i=1 

3 

+ ∑ mpgsin(Ωct +βi +ei) cos(Ωct +βi +ei)cos(ρ)Lp 

i=1 

Detection of events of interest

In this work, a methodology is investigated that can detect 

different events critical for drivetrain health automatically. It is 

assumed that the events that have to be detected are controlled; 

therefore, it is expected that the signatures in the time series are 

similar. As such, a machine learning approach based on pattern 

mining and matching is proposed. 

Pattern mining algorithms have an objective of discovering 

all the different item sets in the given database. As this would 

result in 2N possible subsets for a database with N items, this 

cannot be done using a brute force approach [13]. To this end, 

several algorithms have been proposed in literature to reduce the 

computational effort needed to calculate the frequent patterns, 

such as FP-growth, Prefxspan [14], and Freespan. In this work, 

there is use of a sequential pattern mining algorithm Prefxspan 

to extract the relevant patterns from the learned events. A sequential algorithm is clearly needed in this context to not lose 

the notion of the evolution of the event over time [15]. To move 

toward automatically detecting these events, a user has to manually label multiple in a set of training data. Based on this, the 

3

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frequent patterns are extracted after binning the time series data. 

The less variable the event is, the fewer events need to be labeled by the user to identify the other similar events present in 

the training data automatically. This is implemented in the following way: First, it is determined whether or not a run-up or 

coast-down could be taking place. This can be done by observing if the bin data change in the bin within a certain time horizon. If this is the case, patterns are extracted and attempted to be 

matched with the reference set. To this end, the distance between 

two patterns is defned based on the difference in the number of 

samples in each bin. By summing this difference for all bins and 

by comparing this with a pre-imposed threshold based on the pattern length, similar time sequences can be matched with one another [16]. To make the performance less reliant on a well chosen 

subset of labeled events and to reduce the amount of needed user 

interaction, there is the option to only label a limited number of 

events manually and to implement an iterative procedure. In each 

subsequent iteration, more events will be able to be identifed, allowing in turn to identify more patterns until no additional events 

are being identifed. This makes the algorithm suffciently robust 

in identifying events that have quite a large variability, such as a 

run-up at low wind speed, as will be demonstrated later on. 

The performance of this algorithm will be showcased by 

processing one year of SCADA-1s data of an offshore wind farm. 

For the training stage of the algorithm, an analyst has selected 

several events. One month of one turbine is used to train the 

algorithm. Afterwards, transfer learning is used to process the 

other turbines in the farms. This transfer learning is achieved by 

extracting the set of representative patterns for the feet leader 

and transferring these patterns to the other machines in the farm. 

In this way, high success rates (98+%) can be achieved. 

The learned events consist of turbine starts and stops at both 

low and high wind speeds. The detection takes place using the 

SCADA-1s data, more specifcally the gearbox speed and the 

pitch angles of the blades, as these have proven to produce the 

most representative patterns. For the stops at high wind speeds, 

status logs are used to further specify the reason the turbine 

stopped (e.g., yaw untwist, temperature alarm). These logs are 

not used earlier in the processing chain because they do not register stops at low wind speeds. Furthermore, multiple stops were 

often logged with these codes when only one physical stop had 

taken place. For the low wind speed stops, the wind speed and directions during the stop are extracted to account for the wake effects. By learning typical loading during these events on the feet 

leader, extrapolation can happen to a farm-wide level, as seen in 

Figure 1. Within the context of matching the events of the feet 

leader with the other turbines, pattern mining and matching also 

represent a powerful methodology, as it will allow the expression 

of the similarity between the different time series. Having multiple feet leaders available in the farm will in practice allow reduction of the errors of approximation as it will be possible to learn 

over time which turbines will react more similarly to others in 

the different operating conditions. Because this approach learns 

how the turbine typically behaves during a start-up or coast-down 

in different wind conditions, this technique will remain valid in 

most cases even when faulty machine components are present. 

An exception to this might be faulty behavior at the level of the 

pitch motors. 

RESULTS

DOE 1.5 -MW turbine

As mentioned, it is of interest to be able to assess the bearing 

loading during different turbine operating conditions. As direct 

measurement of the bearing forces is not trivial, there is often the 

option to deduce these from the forces measured at the level of 

the main shaft using strain gauges. This is done using Equation 

1. To guarantee that the turbine was running in steady-state, the 

previously discussed algorithm to detect events was used. In this 

way, different data sets with run-ups presented were identifed, 

as can be seen in Figure 2. The dotted markers indicate the start 

of the event, whereas the starred markers indicate the end. The 

planet forces for one data set where the turbine is operating at 

rated power can be seen in Figure 3. The cyclical nature of this 

force as a result of the carrier rotation can clearly be seen. The 

forces of the three different planet bearings, indicated respectively in blue, orange, and green, are out of phase with respect to 

one another. These bearing forces can then in turn be translated 

to the roller forces. These can then be used in fnite element simulations to refect this to stress distributions in the material. As 

mentioned, this step is strongly problem dependent. For example, within the context of WECs, the infuence of inclusions in 

the micro-structure is of interest because these are hypothesized 

to be critical spots for WEC initiation. 

Time [-]

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Rotor speed [rpm]

FIGURE 2: SEQUENCE OF AUTOMATICALLY LABELED 

RUN-UPS IN THE NREL TEST PROGRAM. 

4

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Having accurate loading information at the level of the bearings is, however, not adequate, as current hypotheses list slip 

as one of the drivers in WEC formation. Therefore, we have 

to also be able to assess the amount of bearing slip during the 

different transients and operating conditions. In [17] and [18], 

an analytical approach to predict roller slip has been presented 

and validated using slip measurements for steady-state operation 

and different events such as LVRTs and emergency stops. These 

were then combined with load information to obtain metrics such 

as the cumulative friction energy and slip power density. It was 

shown that the main contributors to these metrics during steadystate operation are seen during the fast acceleration at the loadzone entry. For the transient events, up to 50% slip is seen. 

FIGURE 3: EVOLUTION OF THE BEARING FORCES 

OVER TIME. 

Offshore farm

Using the described pattern mining approach, one year of 

SCADA data have been processed. An example of the results 

of the algorithm for one day of SCADA data is shown in Figure 

4. The different colors indicate the different classes in which the 

algorithm has classifed the events. A dotted marker is used to 

represent the start of the event, whereas the star indicates the end. 

Among the detected events, no signifcant amount of LVRTs has 

been detected, as the grid is more stable for these offshore farms 

than their onshore equivalent. By running this pipeline across a 

farm-wide level, insights are gained in which events each turbine 

sees throughout its lifetime. 

FIGURE 4: ONE DAY OF SCADA DATA PROCESSED WITH 

AUTOMATIC EVENT DETECTION. TEAL AND PURPLE 

INDICATE STARTS AND STOPS, RESPECTIVELY, AT 

WIND SPEEDS AROUND CUT-IN, AND RED AND GREEN 

INDICATE STOPS ABOVE CUT-IN. 

By comparing the total number of events seen for each turbine, as well as the shape of the individual events, several observations can be made: 

1) Some events such as run-ups at low wind speeds have a 

relatively large variability in their duration and general 

shape. This can be seen in Figure 5, where 150 run-ups 

of the same turbine are shown. These are synchronized to 

coincide at half of the rated speed. Based on this observation, it is expected that the calculated damage metrics 

(e.g., accumulated friction energy) during these events are 

also signifcantly different, showing the need for better 

understanding the sources of this variability and learning 

under which conditions these metrics become signifcantly 

higher to be able to avoid these when possible. 

Furthermore, it can be seen that there are a couple of events 

where the wind speed starts decreasing during the run-up, 

resulting in the turbine doing a coast-down again. Making 

a distinction between these different types is important, as 

there will be signifcant slip and loading at the bearings 

at the moment when the turbine will connect to the grid. 

At the moment of grid connection, there will be large slip 

combined with large loading, meaning that these events are 

potentially more critical for the health of the drivetrain. 

2) There is a signifcant difference in the number of events that 

each turbine within the farm sees. This is shown in Figure 

6, where the total number of run-ups at low wind can be 

seen for a subset of 20 turbines within the farm. No trivial 

correlations were found between the number of events the 

turbines see and the layout of the farm. Besides the number 

5

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FIGURE 5: VARIABILITY IN SHAPE BETWEEN 

DIFFERENT RUN-UPS AT LOW WIND SPEED. 

(AXES NORMALIZED FOR CONFIDENTIALITY) 

FIGURE 6: NUMBER OF EVENTS SEEN FOR THE 

DIFFERENT TURBINES IN THE FARM. 

(AXES NORMALIZED FOR CONFIDENTIALITY) 

of events strongly varying, the percentage of run-ups where 

grid connection took place is signifcantly different across 

the farm. 

Based on the two previous observations, it is clear that the 

two metrics proposed in the literature for WEC formation 

(i.e., accumulated frictional energy and power slip density) 

can vary for the different turbines in the farm. It is also 

important that these events at low wind speeds cannot be detected using conventional SCADA 10-minute and status log 

data. The proposed methodology therefore allows a ranking 

of which turbines are most likely to show WECs in the farm. 

This combined with health metrics obtained with condition 

monitoring allows in turn to get objective information about 

which turbines to inspect. 

CONCLUSIONS AND FUTURE WORK

This paper has presented an approach to automatically detect different events of interest on a farm-wide level. To this end, 

a frst analysis was done on the DOE 1.5-MW turbine. On this 

turbine, events were extracted using SCADA data, and the bearing forces were estimated using strain gauge measurements at 

the main shaft. This was done such that each operating condition 

could be attributed with a damage metric. After this study on this 

single turbine, SCADA data of an offshore wind farm were analyzed. It was shown that the number of these events can strongly 

vary across the different turbines of the farm. As these can be 

critical for drivetrain health, these insights are vital to be able to 

start seeing correlations between failure data and the events seen 

by the turbine throughout its lifetime. 

The future work consists of instrumenting several turbines 

on the offshore wind farm to be able to work with representative 

loading for this turbine and to be able to explore the presented 

feet leader concept in more detail. On the other hand, fnite element models will be developed to predict what is happening at 

the level of the material. These steps will allow validation in 

more detail to the different hypotheses concerning the correlations between WECs and the complex operating conditions of 

wind turbines. 

ACKNOWLEDGMENT

The authors acknowledge the fnancial support of the 

MaDurOS program from VLAIO (Flemish Agency for Innovation and Entrepreneurship) and SIM (Strategic Initiative Materials) through project SBO MaSiWEC (HBC.2017.0606). The 

authors thank their partners for delivering the monitoring data. 

This work was authored [in part] by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, 

LLC, for the U.S. Department of Energy (DOE) under Contract 

No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Offce of Energy Effciency and Renewable 

Energy Wind Energy Technologies Offce. The views expressed 

in the article do not necessarily represent the views of the DOE 

or the U.S. Government. The U.S. Government retains and the 

publisher, by accepting the article for publication, acknowledges 

that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published 

form of this work, or allow others to do so, for U.S. Government 

purposes. 

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