Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective

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1 PEER-REVIEWED Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective Shuangwen Sheng National Renewable Energy Laboratory, Golden, Colorado, USA Received Jan. 18, 2015 Accepted May 23, 2015 Review led by Robert Errichello STLE Editor s Note: Setting up an oil analysis program is fraught with pitfalls; appropriate tests and sampling frequencies must be established for the program to be successful. This month s Editor s Choice paper investigates a variety of online and offline parameters applied to a full-scale wind turbine. A balance must be struck between the high-frequency and relatively less-comprehensive online monitoring, compared to periodic remittal to a fullservice laboratory. Oftentimes historical problems can serve as a guide for selecting the likeliest parameters to monitor, but inconsistent failure rates can make it difficult to establish an appropriate frequency. The findings from this research provide a reasonable view of the benefits of both techniques. Evan Zabawski, CLS Editor KEY WORDS Wind turbine gearbox; oil condition monitoring; oil debris monitoring; oil sample analysis; wear debris analysis ABSTRACT Despite the wind industry s dramatic development during the past decade, it is still challenged by premature turbine subsystem/component failures, especially for turbines rated above 1 MW. Because a crane is needed for each replacement, gearboxes have been a focal point for improvement in reliability and availability. Condition monitoring (CM) is a technique that can help improve these factors, leading to reduced turbine operation and maintenance costs and, subsequently, lower cost of energy for wind power. Although technical benefits of CM for the wind industry are normally recognized, there is a lack of published information on the advantages and limitations of each CM technique confirmed by objective data from full-scale tests. This article presents first-hand oil and wear debris analysis results obtained through tests that were based on full-scale wind turbine gearboxes rated at 750 kw. The tests were conducted at the 2.5-MW dynamometer test facility at the National Wind Technology Center at the National Renewable Energy Laboratory. The gearboxes were tested in three conditions: run-in, healthy, and damaged. The investigated CM techniques include real-time oil condition and wear debris monitoring, both inline and online sensors, and offline oil sample and wear debris analysis, both onsite and offsite laboratories. The reported results and observations help increase wind industry awareness of the benefits and limitations of oil and debris analysis technologies and highlight the challenges in these technologies and other tribological fields for the Society of Tribologists and Lubrication Engineers and other organizations to help address, leading to extended gearbox service life. INTRODUCTION The wind industry has experienced dramatic development in recent years as demonstrated by the globally installed capacity reaching 318 GW by the end of 2013 (Global Wind Energy Council (1)). Despite the progress made by the industry and improvements in turbine design, manufacturing, wind power plant development, and operation and maintenance (O&M), premature subsystem/component failures are still a challenge. With the increase in turbine size and more turbines deployed offshore, these failures, especially those found in the drivetrain (i.e., main shaft bearing, gearbox, and generator), have become extremely costly. To reduce the cost of energy for wind power, 56 Hurricanes are classified into five categories based on their wind speeds and potential to cause damage. Names can be retired if a

2 NOMENCLATURE Analysis Processes, typically certified, used to evaluate properties of the oil or filter samples received by the laboratory, or processes, such as statistical parameters calculation, used to extract information from sensors mounted on the monitored turbine subsystems/components, such as gearboxes. Filter A device that is installed in either the main filtration loop or the kidney filtration loop to remove contaminations from the lubrication oil. Inline When the measurement location is in the main filtration loop. Laboratory A dedicated facility with qualified analysts who conduct oil or filter media sample processing, analysis, and reporting as a business. Offline When the measurement is obtained or analysis is conducted using oil or filter samples taken from either the main filtration loop or the side-stream filtration loop of a test gearbox. Offsite A location that is away from the site where the analyzed oil or filter sample is taken or where the test wind turbine is located. Online When the measurement location is in the kidney loop or side-stream filtration loop. Onsite A location that is the same as the site where the analyzed oil or filter sample is taken or where a test wind turbine is located. Real time Sensor When data are reported at the time a measurement is taken and the delay between these two actions is negligible. A device that detects or measures changes in the lubrication oil properties and indicates the changes through electric signals, which can be interpreted numerically. Wind power plant A grouping of utility-scale wind turbines, each typically consisting of a tower, blades, generator, transformer, and/or gearbox, designed to convert the aerodynamic force from wind on the blades into electricity. A standard wind power plant has a single substation, or more, collecting power from turbines to feed the electric grid. the improvement in turbine drivetrain reliability and turbine availability is critical. Given that each replacement or major repair needs a crane, gearboxes have shown to be the most costly subsystem in a turbine drivetrain to maintain throughout its expected 20-year design life and have become the focus of attention for reliability and availability improvement. To help the industry improve gearbox reliability and turbine availability, a consortium called the Gearbox Reliability Collaborative (GRC) was launched by the National Renewable Energy Laboratory (NREL) in 2007 (Oyague, et al. (2)). It brings together different parties in the gearbox supply chain to openly exchange information and conduct important research and development work with the common goal of improving the reliability of gearboxes. Condition monitoring (CM) work was started under the GRC when it became clear that improved design and manufacturing practices alone cannot address the high downtime challenge caused by gearboxes and that O&M of turbines and gearboxes needs to be improved. CM is one technique for improved O&M, and it can help increase the gearbox reliability if root causes for the detected damage mode are identified and addressed. CM systems normally measure critical indicators of component operation and performance to identify incipient faults before catastrophic failure occurs (Electric Power Research Institute (3)). Although CM has been widely used in other industries and its technical benefits to the wind industry are generally recognized, its adoption and deployment levels are still relatively low in the wind industry. Among various CM technologies applicable to wind turbine gearboxes, currently there are two dominant categories being investigated: vibration-based and oil-related. For vibration-based technologies, the measurements are typically obtained by accelerometers and then processed to generate different signatures for gearbox condition evaluation. These systems can be mounted permanently on the gearbox to continuously collect data or portable devices can be used for periodic data acquisitions. Oil-related technologies can be roughly divided into two subcategories: oil CM and wear debris analysis. The former focuses on the deterioration of oil properties and the latter emphasizes the amount of debris generated and its composition. The typical practice is to collect a periodic oil sample (generally every 6 months) from a gearbox and send it to a laboratory (offsite or onsite) for analysis, which can cover both oil properties and wear debris. Sometimes the sample is a filter element based on which wear debris analysis is conducted. These analyses are classified as offline methods with reference to real-time oil debris and CM sensors, which can be deployed in the main filtration loop of the gearbox inline, or in its kidney or side-stream filtration loop online (Roylance (4)). A majority of these real-time sensors target wear debris counts, including ferrous or nonferrous, and some new sensors monitor oil properties, such as viscosities, contamination, hurricane is particularly large and destructive. Retired hurricane names include Andrew, Camille, Bob, Fran, Katrina, Hugo and Sandy. 57

3 Figure 1 NREL dynamometer test facility with one test gearbox installed, NREL and relative moisture levels. These real-time sensors, either inline or online, are typically installed in the gearbox filtration loop, main or side-stream, and collect data continuously. As with most technologies, each CM technology has its own advantages and limitations. The main advantages of vibration analysis lie in its capabilities in pinpointing the damaged component and its location. Its main limitation is that successful detection rates for low-speed stage components may be low. Comparatively, oil and wear debris analysis is powerful in confirming component damage by monitoring wear debris, and oil analysis can detect condition deterioration, caused by either loss of additives or contamination; however, if some components inside the gearbox, such as gears and bearings, have a common metal element it is difficult to identify which specific components are damaged based only on oil and debris analysis. Given the fact that wind turbine gearboxes are complex and can fail in dramatically different ways, an integrated approach, combining at least one vibration-based and one oil-related technology, is recommended (Sheng (5)). Despite the fact that oil and debris analysis-based CM has been investigated during the past few decades for traditional applications, it is relatively less accepted by the wind industry compared to vibration-based technologies. This is evidenced by the lack of dedicated reviews on oil and debris analysis-based CM for wind turbines as most of the focus is on vibration-based technologies (Amirat, et al. (6); Hameed, et al. (7)). Furthermore, the few published works on wind turbine oil and debris CM (Tan, et al. (8); Zhu, et al. (9)) were not based on full-scale wind turbine or gearbox testing; however, given the fact that a wind turbine has so many lubricated components, such as gearboxes, main shaft bearings, pitch and yaw bearings/gears, and generator bearings, it is critical for the industry to pay close attention to lubrication including lubricant sampling and analysis so that a turbine s subsystems/components can be maintained properly and their service lives extended. This article discusses oil and debris analysis-based CM and presents results obtained from testing full-scale wind turbine gearboxes rated at 750 kw. The test gearboxes consist of one planetary and two helical stages and are representative of megawatt-scale wind turbine gearboxes. The gearboxes were tested under three conditions: run-in, healthy, and damaged. The reported CM technologies include offline, online, and inline methods. The reported results and observations can help inform the wind industry on the benefits and limitations of oil and debris analysis technologies. This article can also advise the Society of Tribologists and Lubrication Engineers which has mostly been dealing with traditional machinery (Gudorf, et al. (10); Choy, et al. (11)) on some unique aspects of wind turbine applications so that future research and development may help address the wind industry gearbox reliability challenge from the oil and debris analysis perspective. This article is organized as follows: first, a brief overview of the 2.5-MW dynamometer test facility, test gearboxes, tests conducted, gearbox oil, and wear debris analysis instrumentation will be introduced. Next, the results will be presented in three categories based on the test gearboxes being classified as run-in, healthy, or damaged. Finally, the article concludes with a summary of observations based on the testing results and recommendations for some future research and development opportunities with oil and debris analysis for wind turbine gearboxes. APPARATUS AND PROCEDURE Test setup The tests described in this article were conducted in the 2.5- MW dynamometer test facility located at the NREL (Musial and McNiff (12)). The dynamometer is composed of a 2.5- MW induction motor, a three-stage epicyclic speed reducer, and a variable-frequency drive with full regeneration capacity (NREL (13)). It is capable of providing a rated torque up to 1.4 MNm or a rated power limited to 2.5 MW, with speeds varying from 0 to 30.0 rpm to a test article. The dynamometer is equipped with nontorque loading actuators, rated up to 440 kn for radial load and 156 kn for thrust load, which can also be utilized to apply thrust and bending loads to the test article to simulate typical loads seen in the field by a wind turbine. The GRC started with two 750-kW wind turbine gearboxes of the same design with the intention to test one in the dynamometer and the other in a wind plant as a comparison. To make the GRC research representative, the test gearboxes were redesigned to reflect the configuration and characteristics of popular gearboxes installed in megawattscale wind turbines (Link, et al. (14)). The design has one planetary and two parallel stages, a floating sun, cylindrical roller planet bearings, tapered roller bearings at the downwind side of the parallel stages, a pressurized lubrication system, a side-stream or kidney filtration loop, a heat exchanger, and a desiccant breather. For simplicity, these two test gearboxes are named gearboxes 1 and 2, respectively. Figure 1 is a photo of the dynamometer test setup with one test gearbox installed (Dempsey and Sheng (15)). During the past few years, many tests have been conducted on these two GRC test gearboxes, and those related to oil and wear debris analysis reported in 58 OCTOBER 2016 TRIBOLOGY & LUBRICATION TECHNOLOGY

4 Table. 1 Oil and wear debris analysis-related tests. When Objectives Designation April July 2009 October December 2009 June August 2010 September 2010 Nov 2013 Present Controller shake down Run-in Run-in Static nontorque loading in limited directions Static nontorque loading in any direction Dynamic nontorque loading and dynamic torque Compare as-built and damaged behavior; compare gearboxes 1 and 2 Collect condition monitoring data on damaged gearbox Test of nontorque loads: static and dynamic bending moments; static and dynamic thrusts; misalignments Collect condition monitoring data on a healthy gearbox Phase 1 gearbox 1 run-in Phase 1 gearbox 2 run-in Phase 2 gearbox 2 healthy Phase 2 gearbox 1 damaged Phase 3 gearbox 2 healthy this study are listed in Table 1. They are grouped into three categories of the test gearbox condition as run-in, healthy, and damaged. The test gearbox 1 was run-in at the 2.5-MW dynamometer test facility and later sent to a nearby wind plant for field testing, during which two unexpected oil loss events occurred, leading to damaged gearbox components (Errichello (16)). A photo of the severe scuffing that occurred on the high-speed gearset is shown in Figure 2. Figure 3 shows the lubrication system used during the Figure 2 High-speed shaft gearset damage, NREL phase 3 gearbox 2 testing. The top portion represents the inline filter loop, which is composed of an oil pump, followed by a two-stage filter of 50 and 10 m and a heat exchanger. It is the same as those used during the other tests as listed in Table 1. The bottom portion represents the kidney loop, which is composed of a pump and a filter of 3 mm. It represents the latest configuration, in which the location of online sensors was changed from what was used during the earlier phases of the tests trying to get improved responses from all sensors. Instrumentation Throughout the entire testing period, various oil and wear debris analyses were conducted via different types of instruments. Based on the measurement or analysis location with Figure 3 Diagram of the lubrication system used during phase 3 gearbox 2 test. 60 Most hurricanes die at sea when they pass over areas of cooler water.

5 reference to the test gearbox, these instruments can be classified into three categories as inline, online, and offline. Almost all of the measurement or analysis results can be remotely accessed either in real time or when they become available using a dedicated program or simply a Web browser. This enables the remote equipment condition diagnostics, CM system troubleshooting, and data analysis. Inline instruments were mainly oil debris sensors, as illustrated by K1 in Figure 3. This type of sensor does not measure the health of oil but the condition of the monitored equipment (i.e., gears or bearings inside wind turbine gearboxes), through identification and trending of ferrous and nonferrous wear debris shed from contacting surfaces of these components and carried in the oil. The sensor K1 used in this study is a full-flow inductive device and can be installed permanently either before or after the pump but always before the filter (Dupuis (17)). Whenever a ferrous or nonferrous wear particle larger than a certain threshold in size passes through the sensor, the magnetic field formed inside the sensor is disturbed and an electric pulse is generated and counted. The counts over time represent the cumulative damage that occurred to the monitored components (e.g., bearings or gears inside of wind turbine gearboxes). The minimum detectable ferrous wear debris size for this type of sensor can be down to 100 mm and, for nonferrous wear debris, down to 300 m. Online instruments may refer to a few different types of sensors. Normally these sensors can be divided into three types: oil debris, condition, and cleanliness level. The online sensors investigated in this study are illustrated in Figure 3 by K2 to K5, among which K2 and K3 are wear debris sensors, K4 is an oil condition sensor, and K5 is an oil cleanliness-level measurement sensor. The sensing principle for K2 and K3 is similar to K1 but with a relatively smaller size for the minimum detectable ferrous wear debris of about 30 to 50 m, as a result of relatively smaller bore size, slower flow rate, and lower oil pressure in the kidney loop than in the main filtration loop. For nonferrous wear debris, the minimum detectable size can be down to 130 to 150 m. Measurements of both ferrous and nonferrous wear debris can be grouped into different size bins and trended. These sensors are typically placed ahead of a filter that captures the debris after it is measured by the sensors. The sensing principle for K4 varies depending on what parameter it uses as a measure of the lubricant condition. In this study, the sensor K4 is composed of a suite of sensors measuring relative humidity of the oil (dissolved water), oil quality (changes with the level of such contaminants as soot, oxidation products, glycol, and water), and oil temperature. The sensing principle for oil cleanliness-level sensors like K5 investigated in this study is typically based on the light obscuration method, which has a light source transmitting through the oil flow and detected by a photodetector. Particles in the oil obscure the transmitted light and cause the photodetector to spike according to the number and size of particles. Based on the amplitude of the spike, a particle is classified into an appropriate size bin. In the wind industry, the classification is typically made according to the International Organization for Standardization (ISO) cleanliness level indicating the amount of particles seen in 1 ml of monitored lubricant that can be classified into three size bins: >4 m, >6 m, and >14 m (ISO 4406:1999(E) (18)). Typically, an online wear debris sensor like K2 or K3 is permanently installed in a wind turbine gearbox and provides information on the monitored gearbox in real time. Online oil condition sensors like K4 are still in the developmental or trial stage in the wind industry. The oil cleanliness level sensors like K5 can be installed permanently in a wind turbine gearbox, used on the oil before it is put in a gearbox, or used as a portable unit to periodically check the oil cleanliness levels. Keeping oil clean and dry is very critical for achieving the gearbox expected performance and extending its service life (Muller and Errichello (19)). Offline instruments, depending on the location of the oil sample or debris analysis, can be further divided into onsite and offsite subcategories. Currently, it is a typical practice for wind plant owners and operators to send an oil sample collected from a turbine at about 6-month intervals to a dedicated offsite laboratory for offline analysis. If a wind plant owner and operator has a fleet of turbines, it might be economical to invest in a few oil and debris analysis instruments and dedicate some human resources for conducting the needed analyses. In this case, the oil or debris analysis is conducted on the wind plant where the test turbine is located (offlineonsite analysis; Sheng, et al. (20)); however, offline-onsite practice has not become common in the wind industry yet, and whether it will is heavily dependent on the instrumentation cost, number of turbines in a fleet, and availability of qualified personnel. In addition to oil samples, the owner and operator sometimes also conduct filter element analysis (Sheng, et al. (21)). It has been observed that traditional oil sample analysis is good for monitoring the deterioration of the oil s condition but is not effective for detecting component damage, which can be complemented by filter element analysis. The parameters typically evaluated in an oil sample analysis for the wind industry include viscosity, acid number, particle counts, water content, additive levels, and wear index. Sometimes elemental analysis is conducted on particles that are contained in the oil sample or trapped by the filter element. It is worth noting that these analyses are best conducted by following procedures specified by American Society for Testing and Materials standards, but it is hard to completely eliminate inconsistent practices among different laboratories. RESULTS AND DISCUSSION Run-in: online oil cleanliness level measurements The first set of results was obtained by online oil cleanliness level measurement sensors during the run-in of gearbox 1 (illustrated in Figure 4 on Page 62). The generator status is labeled in Figure 4 and 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 grid, but the oil cleanliness-level mea- TRIBOLOGY & LUBRICATION TECHNOLOGY OCTOBER

6 Figure 4 Online oil cleanliness-level measurements during run-in of test gearbox 1. Figure 5 Online oil cleanliness-level measurements during run-in of gearbox 2. surement sensor was left running. At around 14:34, the power supply to the sensor was shut off and all oil cleanliness-level readings dropped to 0 values. The cleanliness levels are expressed according to ISO 4406:1999 (18) in Figure 4. The figure shows that throughout the gearbox operational process, a broad range of particle sizes was generated, as demonstrated by the increased ISO 4406 readings in all three size bins: >4 m, >6 m, and >14 m. It was also observed that the oil cleanliness-level readings increased with the ramping up of the generator speed and decreased with the shutdown of the generator and continuously functional filtration system. Based on these observations, it is hypothesized that the oil cleanliness level can potentially be used to control and monitor the run-in of wind turbine gearboxes. The expectation is that the readings will increase when the run-in at a certain load level starts, and they will gradually stabilize because of the smoothed contacting surfaces obtained through run-in and the continuously functional filtration system. To evaluate this hypothesis, the oil cleanliness level measurements obtained during the run-in of test gearbox 2 were also collected and illustrated in Figure 5. Approximately between 14:05 and 16:29, the oil cleanliness level readings first increased and then stabilized, which is consistent with the expectation provided earlier. The entire period between 14:05 and 16:29 can then be considered as the run-in time for test gearbox 2 at a 100% load level. The stopping time of 16:29 may vary a bit depending on what criterion was used to define a balance between debris generation through the run-in and the filtration system removal. It is worth pointing out that both tests used two filtration systems: a main loop (down to 10 m) and a kidney loop (down to 3 m). If there was only the main filtration loop filtration system (down to 10 m) available during the run-in, the changes in oil cleanliness level readings would mainly be reflected in the largest size bin >14 m, and this bin could determine whether a run-in at a certain load level was complete. When using oil cleanliness-level measurements to control and monitor wind turbine gearbox run-in, it is important that the filtration system functions as designed and its performance is consistent throughout the entire run-in period for a certain load level; various filtration systems used during the run-in may lead to different run-in time intervals. In any case, this is a valuable contribution to the wind industry because no standards are available that specify how to monitor and control the run-in of wind turbine gearboxes, and different gearbox manufactures may have different practices. Another observation from Figures 4 and 5 is that the oil cleanliness-level readings increase during major transient events with the generator online action in Figure 4 and the generator online and test stopped actions in Figure 5. It is therefore reasonable to conclude that wind turbine gearbox wear is mostly caused by major transient events, assuming that the gearbox is designed and manufactured to acceptable industry standards. Run-in: inline and online oil debris counts The second set of results was obtained by oil debris counting sensors and is illustrated in Figures 6 9. The figures show that the entire testing lasted about a month. Specifically, the run-in was conducted at four different load levels and two speeds: 25% rated torque, 1,200 rpm; 50%, 1,800 rpm; 75%, 1,800 rpm; and 100%, 1,800 rpm. Comparing the results in Figures 6-8, which include both ferrous and nonferrous debris, it is observed that the absolute particle counts are different among sensors K1 ( 900), K2 ( 270), and K3 ( 63), which can be attributed to their different mounting locations, flow rates, bore size, minimal detectable particle size, etc. However, if the attention is shifted to the periods when generation rates of debris increase, as highlighted by dashed ellipses in these figures, it is seen that about the same periods are indicated by all three sensors. This implies that if the oil debris sensors are used to detect changes in trends of wind turbine gearbox debris generation, both inline (e.g., K1) and online sensors (e.g., K2 and K3) can be effective. An inline oil debris counting sensor has an advantage if a prognosis of the remaining useful life of the gearbox is expected and both are assumed to have the same level of debris particle detection capabilities because it may 62 OCTOBER 2016 TRIBOLOGY & LUBRICATION TECHNOLOGY

7 y Figure 6 Cumulative particle counts obtained by sensor K1 during run-in of gearbox 2. Figure 8 Cumulative particle counts obtained by sensor K3 during run-in of gearbox 2. Figure 7 Cumulative particle counts obtained by sensor K2 during run-in of gearbox 2. Figure 9 Ferrous particle counts by K2 divided into five sizes of bins during run-in of gearbox 2. be easier to develop a prognostic model using an inline sensor with full oil flow than an online sensor with a side stream of oil flow, because the correlation between the online sensor counts and the gearbox deterioration has to be developed under the influences of reduced flow rates, pressure, and bore size in the kidney loop. Thresholds for both inline and online oil debris counting sensors are used to provide maintenance recommendations. These thresholds may vary from turbine to turbine, gearbox to gearbox, and even site to site, so careful attention is needed when applying the sensors to different gearboxes. In Figure 9, the breakout of ferrous particles counted by sensor K2 into five different size bins is illustrated. It provides more details on the distribution of debris size than those cumulative counts as shown in Figures 6-8 and can facilitate analyses that take debris size bins into account. Throughout this period of testing, the majority of debris generated was in the 100 and 800 mm range. The most debris generated was in the 200 and 400 mm range. It can be concluded that large debris can be generated during run-in of a wind turbine gearbox. Given that the hardness of the debris is comparable to the contacting surfaces of gears and bearings inside the gearbox, it is critical to have an appropriate filtration system capable of removing the debris during the run-in process. Most wind turbine gearbox manufacturers have adopted the above described run-in practices; however, run-in of wind turbine gearboxes has not become an industry standard practice yet. It is thus beneficial for a turbine original equipment manufacturer or a turbine owner and operator to check the oil cleanliness level after a gearbox is received. This can be done through an offline oil sample analysis or a portable system that can provide oil cleanliness level measurements. Based on Figures 4 and 5, the oil cleanliness-level readings are shown to be strongly affected by the testing conditions, making it hard to extract information solely caused by component wear and evaluating gearbox health condition. Comparatively, the oil debris counts shown in Figures 6-9 only change when debris are generated. The oil debris counts appear to be better indicators to monitor damage occurring to gears and bearings in a wind turbine gearbox than oil cleanli- 64 The worst hurricane damage is often caused by a storm surge. A storm surge is like a giant wall of water pushed on shore by hurricane winds.

8 Table 2. Oil samples taken during run-in of gearbox 2. Sample Number Sampling Date Sampling Time Operating Hours 1 11/3/2009 2:30 p.m /5/2009 2:13 p.m /10/2009 3:20 p.m /11/ :55 a.m /17/2009 4:49 p.m /17/2009 6:10 p.m /18/2009 3:40 p.m /19/2009 5:00 p.m /3/ :15 p.m ness levels. This is true because turbine operational condition may have significant influences on oil cleanliness and makes it hard to establish a clear correlation between component deterioration and the oil cleanliness change, which typically gets worse as oil ages. This, however, does not diminish the value of using oil cleanliness level to monitor the contamination of wind turbine gearbox oil and its potential use to control and monitor run-in of wind turbine gearboxes. On the other hand, the oil debris counts may not be directly used to control and monitor run-in of wind turbine gearboxes. Whether the debris generation rates based on oil debris counting sensors can be used to monitor and control run-in of wind turbine gearboxes may be worthy of future investigations. Run-in: offline oil sample and wear debris analysis Offline oil sample and wear debris analyses, as a typical CM practice used by the wind industry, were conducted through the testing of gearboxes 1 and 2 to evaluate its effectiveness. As an example, the results obtained during the run-in of gearbox 2 are discussed in this section. There were nine oil samples taken throughout the entire run-in of gearbox 2 as detailed in Table 2. To obtain more details on oil and debris condition changes throughout the run-in of a wind turbine gearbox, the sampling intervals were much shorter than 6 months a typical practice followed by the wind industry. It is worth noting that the total number of operating hours (13) cannot be simply treated as the accumulated time spent on the run-in of test gearbox 2 because a few other tests were also conducted, but a majority of the time was used to run-in gearbox 2. Table 3 shows some of the oil and debris analysis results based on one reference oil sample and five recent samples 5 to 9 as defined in Table 2. The left-most column shows the parameters evaluated along with their units or analyses conducted, and the next two columns are lower and upper bounds corresponding to the evaluated parameters, which are typically provided by the oil analysis laboratory. In addition to oil condition, the parameters evaluated in this work included total acid number (TAN, mg KOH/g), viscosities (mm 2 /s or cst) at 40 and 100 C as shown in Table 3, sulfur (percentage), water content (ppm) by Karl Fischer, and a few others that are not shown, such as particle counts through light blockage method (LBM) which is often called the light obscuration method and the same as what is followed by typical cleanliness-level measurement sensors like K5 and corresponding codes according to ISO 4406:1999, and oxidation-level estimation using remaining useful life evaluation routine. The particle counts by LBM will be illustrated in the next section through a comparison with those obtained by scanning electron microscopy (SEM) and LaserNet Fines Table 3. Offline oil sample and wear debris analysis during run-in of gearbox 2. Analysis Results Lower Bounds Upper Bounds Reference Oil Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 TAN mg KOH/g 0.16 <0.10 <0.10 <0.10 <0.10 <0.10 <0.10 Direct Reading (DR) Ferrography DR Ferro DL /ml DR Ferro DS /ml DR Ferro WPC /ml Dilution Factor DR Ferro Percentage of Large Particles (PLP) DR Ferro Severity Wear Index (SWI) Viscosities 40 C C Metals Iron ppm 2 < Silicon ppm 20 < Zinc ppm Phosphorus ppm Calcium ppm Barium ppm Molybdenum ppm < Particle Quantifier (PQ) Wear Index Sulfur wt% Water by Karl Fischer (KF) ppm Outside Cautioning Limit Outside Alarming Limit 66 OCTOBER 2016 TRIBOLOGY & LUBRICATION TECHNOLOGY

9 (LNF; Spectro Scientific (22)). The debris analyses conducted include direct reading ferrography, analytical ferrography, and Fourier transform infrared spectroscopy. The analytical ferrography (AFG) will be illustrated in the next section through a comparison between its inferred damage modes and those obtained through SEM and LNF. Because of the research nature of this project, the analysis methods investigated are more than what are typically adopted by the wind industry. As shown in Table 3, only viscosities have both lower and higher bounds, and most of the other parameters have only upper bounds. Whenever the analysis of a sample results in readings close to or outside of these bounds, the laboratory that analyzed the sample will provide a certain level of warnings to the end users. For example, the readings of the percentage of large particles under direct-reading ferrography were close to or above its corresponding upper bound and the viscosities at 40 C were smaller than its corresponding lower bound. As a result, these readings are highlighted in the report by a single star (cautioning) or double stars (alarming). The other parameters shown in Table 3 are within their expected ranges. As discussed earlier, there are a few benefits of having oil and debris analyses conducted by a dedicated laboratory. For example, the access to more samples taken from the same type of monitored equipment enables analysts to determine representative thresholds for various parameters used to evaluate oil and the monitored equipment deteriorations. In addition, the analyses in most laboratories are typically conducted by qualified personnel and by following standardized or certified procedures, helping to minimize variations introduced during the analysis processes. Finally, the analysis results produced by most laboratories are interpreted and the corresponding maintenance recommendations are provided by certified lubricant analysts, helping to minimize possible reporting of incorrect information and subsequent decision making. Run-in: offline image-based cleanliness levels and damage modes One limitation with oil and debris analysis conducted by a dedicated laboratory is that it often takes longer time to get the results before appropriate O&M actions can be taken because of the extra sample shipment time needed. If the end users would like to get results and take actions immediately, one solution is to have some onsite equipment that can provide oil and machine condition information quicker by eliminating the sample shipment time. Among the various onsite equipment options, image-based particle shape and size analysis systems appear attractive, because they not only provide particle counts, which can be coded according to ISO 4406:1999 (18), but they show particle types and correspondingly inferred damage modes. Depending on the techniques used, some particle shape and size analysis systems can even be used to conduct elemental analysis and determine the material composition of measured particles. By using the techniques adopted by a dedicated laboratory as benchmarks, a Figure 10 Offline image-based technologies: oil cleanliness level for size bin greater than 14 m during run-in of gearbox 2. comparison against two types of image-based particle shape and size analysis systems was conducted based on oil samples collected during the run-in of gearbox 2 and is discussed in this section. One LNF and one automated SEM (Drake (23)) were investigated. The LNF uses a laser imaging technique to identify the type, rate of deterioration, and severity of mechanical faults by measuring the size distribution, rate of progression, and shape features of wear debris in lubricating fluids (Spectro Scientific (22)). The SEM uses dynamic beam scans of filter samples to detect and characterize particles. Each system s ability to attain ISO 4406:1999 (18) cleanliness levels and its ability to infer potential damage modes or particle types were compared with the equivalent results obtained by the laboratory. In the laboratory, the LBM was used to attain ISO cleanliness levels and the AFG was used to identify particle types or damage modes. To perform AFG, solid debris suspended in a lubricant is separated and systematically deposited onto a glass slide, which is then examined under a microscope to determine particle size, composition, and surface condition of both ferrous and nonferrous particles (Barrett and McMahon (24)). Each of the nine oil samples was measured at least three times and the averages of their ISO cleanliness-level readings were used as the final results from the LNF and the SEM. The results for bin 14 m obtained by all three techniques (i.e., the LNF, SEM, and LBM) are shown in Figure 10. It was observed that all three methods showed a trend of oil getting cleaner throughout the run-in. This outcome is consistent with the expectation of the oil cleanliness level trend throughout the run-in of a wind turbine gearbox and having the run-in debris removed by filtration. The highest reading obtained by the LBM (based on sample 2, as highlighted in Figure 10) turned out to be silicon. The variations in readings from all three methods, based on seven out of the nine samples, excluding samples numbered 2 (largest variation) and 4 (smallest 68 Hurricanes also can produce tornadoes. A 1967 hurricane in Texas caused more than 140 twisters.

10 Figure 11 Offline image-based technologies: damage modes during run-in of gearbox 2: (a) AFG, (b) LNF, and (c) SEM. variation) as outliers, were between two and four orders of ISO 4406:1999 (18) cleanliness codes. The results demonstrated that, when looking at ISO cleanliness levels, it is more important to focus on one technology and the overall trends than the absolute cleanliness code values, assuming that the variations with analysis procedures and personnel are minimized by the averaging process based on multiple analyses. In addition, it is important to examine possible outliers and determine whether they represent the true condition of the analyzed oil. In addition to the ISO cleanliness levels, the inferred particle types or damage modes obtained from each technique were also compared and the results are shown in Figure 11. Figure 11a shows that the top two damage modes identified by AFG are 70% rubbing and 15% corrosion. If rubbing is classified as normal wear and the rest as abnormal, the result shows 30% being abnormal. In Figure 11b, the top two portions identified by the LNF are 65% fatigue and 14% fiber. If nonmetallic ingredients and fiber are not considered as abnormal metal wear and the rest is, the result shows that 79% is abnormal. In Figure 11c, the top two portions identified by the SEM are 47% nonmetallic and 38% fatigue. If nonmetallic ingredients are not considered abnormal, whereas metal wear is, the result shows 53% as being abnormal. It is clear that the findings from different techniques are inconsistent, which may be attributed to a few factors: (1) incomparable terminologies used by each technique and (2) variations in sample preparations, analysis processes, and equipment capabilities, etc. To address the incomparable terminology challenge, a standardizing effort is needed so that all technologies can adopt or at least perform a cross-check among terminologies to enable comparable interpretation of analysis results. For the other variables caused by the analysis personnel and equipment, a certified analyst and procedure based on calibrated instruments, as well as multiple analyses to reduce possible extremes caused by single analysis, will help. The analyzed sample being in-service and not calibrated with known particle types or damage modes makes it difficult to evaluate which one of these three techniques most closely reflects the real condition. Therefore, a comparison based on known particle types or damage modes in a sample will be worthwhile. It is worth noting that using the image-based technologies investigated herein, additional in-depth elemental analyses are needed to pinpoint the failed components. 70 OCTOBER 2016 TRIBOLOGY & LUBRICATION TECHNOLOGY

11 Based on the oil cleanliness level and particle types or damage modes results, it is reasonable to conclude that imagebased technologies can be used onsite to monitor oil cleanliness levels as long as the focus is on overall trends. However, more evaluation is needed to conclude whether these technologies can be used onsite to reliably evaluate the monitored equipment s condition based on their inferred particle types or damage modes. One challenge is the descriptions of particle types or damage modes need to be standardized or made comparable among different technologies. For particle counts through oil sample or debris analysis, whether offsite or onsite, attention needs to be paid to sample taking, preparation, and analysis procedure (Caldwell (25)); derive final results through multiple analyses, if possible; and focus more on overall trends of analysis results and less on absolute values. Healthy: oil cleanliness level measurements Based on test gearbox 2, which was considered healthy, a round of CM data collection was conducted during the course of one day. The test was started around 13:45 and stopped around 15:30. The gearbox was first tested under 25% rated torque at 1,200 rpm and then shut down and ramped back up to 1,800 rpm, under which a loading sequence was applied. The load was kept at 50% rated torque for about 20 min, then at 100% rated torque for another 20 min, and finally down to 50% rated torque for about 10 min. The data presented in this and the next section were mainly collected under this loading sequence with the intention to evaluate whether certain measurements are heavily affected by loading conditions. The online oil cleanliness level measurement results are shown in Figure 12. The data before the first transitional period, around 14:10, were collected at 25% rated torque and 1,200 rpm, and those after the fourth transitional period, around 15:30, were collected while the test gearbox 2 was shut down with the kidney loop filtration system left running. Comparing the readings from all three curves (size bins), it can be observed that most of the particles are of smaller sizes, no greater than 14 m. In addition, the bin of greater than 14 m is affected by the loading sequence more than the other two, which implies that for wind turbine gearbox component deterioration evaluation purposes, more insight may be gained from the bin greater than 14 m. However, serious attention has to be paid to the operational condition because of its likelihood to influence the oil cleanliness-level measurement results. In addition, the readings of the bin greater than 14 m always jump up first during the transitional periods, as highlighted by the short red arrows in Figure 12, and then settle. This provides a piece of evidence that most wind turbine gearbox component deterioration is caused by transient events. Comparing the readings at each load level after they settle down, it appears that they do not necessarily increase with load levels, and these periods may be useable for evaluating damage to monitored components according to oil cleanliness level measurements. Figure 12 Online oil cleanliness-level measurements during phase 3 gearbox 2 testing. Figure 13 Online oil condition measurements during phase 3 gearbox 2 testing. Healthy: online oil condition measurements The oil condition sensors investigated during this round of testing provide measurements on oil moisture, temperature, quality, and environmental temperature and moisture. The environment temperature and moisture are useful to more accurately interpret the changes in oil condition measurements. Figure 13 shows the online moisture and temperature measurements (the left vertical axis is moisture in percentage relative humidity and the right vertical axis is temperature in Celsius) of both the oil and the environment. It is seen that the environmental temperature did not change much and the environmental moisture first experienced an increase and then a decrease throughout the entire data collection period. The oil moisture changes inversely with the oil temperature. Neither the oil moisture nor the oil temperature appears heavily influenced by the environmental temperature or moisture, 72 Slow-moving hurricanes produce more rainfall and can cause more damage from flooding than faster-moving, more powerful hurricanes. Hurricane

12 and the reason might be that the data collection period is too short and has not got to the weekly or monthly level. The oil moisture and temperature also appear less correlated with the changes in load levels as demonstrated by a reduction of oil moisture when the load is increased from 50% rated torque to 100%, and another reduction not an increase, when the load is decreased from 100% rated torque to 50%. The reason might be that oil temperature and moisture are slowchanging variables and more correlated with cumulative not transient load changes. Figure 14 shows the oil quality measurement results and the unit of its vertical axis is Q, customized by the sensor provider. The oil quality changes with the level of contaminants such as soot, oxidation products, glycol, and water. The increase in oil quality value indicates the deterioration of oil quality. Figure 14 shows that throughout the entire data collection, the oil quality appears to be getting better (values become smaller). It is counterintuitive, but it is likely that the oil quality measurement is highly affected by oil temperature and moisture, which experienced pretty dramatic changes, as shown in Figure 13. When comparing the oil quality measurement with the changes in load levels, it appears that the oil quality improves no matter load increases or decreases and no clear correlations can be established between these two. In summary, online oil condition measurements appear to be affected more by cumulative effects of wind turbine operations than transient events, such as instant changes in operational load levels. Long-term testing is needed to evaluate whether online oil condition measurements are effective. Figure 14 Online oil quality during phase 3 gearbox 2 testing. Figure 15 Inline oil debris sensor measurements during phase 2 gearbox 1 testing. Damaged: inline oil debris counts Based on the damaged gearbox 1, CM data collection was conducted during phase 2 of the GRC tests. The inline oil debris sensor counts obtained during this round of testing are presented in Figure 15, which shows that within 3 days, the counts increased from 0 to about 680. For September 16 alone, the particle generation rate reached about 70 particles per hour. As a reference, there were about four particles counted during one day by the same type of sensor installed in the same location based on healthy gearbox 2. It is reasonable to conclude that, in a damaged wind turbine gearbox, debris generation rates tend to increase dramatically. Damaged: offline oil debris analysis After the test of damaged gearbox 1 in the NREL 2.5-MW dynamometer was completed, it was shipped to a gearbox rebuild shop and disassembled. During its disassembly process, the gearbox oil was drained through a few filter cloths, one of which was sent to a dedicated laboratory for a SEM analysis. Automated feature analysis was used by the laboratory in conjunction with the SEM to quantify and measure the debris particulate and identify major chemical classifications. The filter cloth was immersed in a solvent and agitated in an ultrasonic sink. Then, 20 ml of the solution was drawn through a filter and analyzed. Figure 16 shows the distribution of chemical classifications of the debris particulates. The classification of one debris particulate into a certain chemical is made by identifying trends in its composition and examining whether the trends match criteria used to define that chemical. For example, if the particulate has more than 30% iron, it will be considered steel (Herguth (26)). Figure 16 on Page 74 shows that the major particulate constituents in the specimen are steel, iron oxide, brass, and zinc, which possibly originate from bearings, gears, or oil additives. By comparing these constituents with the damage that occurred to the test gearbox 1, as illustrated in and discussed in the failure analysis report (Errichello (16)), their corresponding sources can be determined. For example, the major source of steel is gears, and iron oxide is a result of high temperature caused by oil starvation, etc. If the sample were taken from an in-service turbine, the laboratory would recommend that the turbine owner and operator take needed maintenance or corrective actions, such as conducting a bore-scope inspection of the gearbox to verify possible gear or bearing damage, tracking down what might have led to the high temperature, and taking needed actions to correct it, according to the analysis results. Floyd off the coast of Africa was barely a category I hurricane but still managed to mow down 19 million trees and caused a billion dollars in damage. 73

13 Figure 16 Particulate chemical classification distribution obtained from phase 2 gearbox 1. The classification of one particulate into a certain chemical can be further illustrated by examining backscatter electron image (showing brightness relative to atomic number and density) of the particulate and its associated X-ray spectra. Figure 17 shows an image of a large steel particle obtained from the analyzed specimen and its corresponding X-ray spectra. The X-ray data were collected from the area marked in the image. The iron content of the particulate is obviously greater than 30% and the particulate should be classified as steel. By following this procedure to automatically process all particles in the specimen, very detailed and valuable information can be obtained, enabling the generation of a lot of insightful reports (e.g., Figure 16). The particulates were also sorted by size using equivalent diameter (the diameter of a circle with the same area as the area of the particle) measured as the criteria. Figure 18 summarizes the results and the horizontal axis shows different size bins. Corresponding to each size bin, the vertical axis represents the percentages of each chemical with reference to the total debris of the specimen. The numbers above the bars for each size bin represent the sum of percentages of all identified chemicals from the specimen. As shown, 75.1% of the particles were <4 m in equivalent diameter and 1% were >25 m. These results imply that the oil cleanliness levels evaluated by ISO 4406:1999 (18) only counted for about 24.9% of the particulates in the specimen, because its smallest size bin is >4 m. In addition, caution needs to be paid to these particulate sorting results, which only represent the analyzed specimen and not the real gearbox condition, because a majority of the debris, especially those of large sizes, might have been removed by the filtration systems. However, in a typical oil debris analysis conducted based on oil samples, this will not be a problem because the samples are typically taken before the oil is filtered. Figure 17 Backscatter electron image of a large steel particle obtained from phase 2 gearbox 1. In summary, the SEM with automated feature analysis is a powerful tool for debris analysis, which is effective for monitoring wear and contamination in wind turbine gearboxes. Once data indicate an abnormal condition, further investigation is often warranted to identify possible root causes and determine reasonable corrective actions. Conclusions and recommendations Some first-hand oil and wear debris analysis based on testing of full-scale wind turbine gearboxes is presented in this article. The investigated techniques cover a few different types including real-time inline and online oil condition and wear debris monitoring, as well as offline-onsite and offsite oil sample and wear debris analysis. Based on the results obtained from the dynamometer testing of two 750-kW test gearboxes under three different conditions: run-in, healthy, and damaged, it is reasonable to conclude that online oil cleanliness level measurements: - can be used for monitoring and controlling the runin of wind turbine gearboxes. This can potentially be introduced as a standard practice to the wind industry. - are greatly affected by turbine operational conditions. Effective methods have to be used to filter out 74 OCTOBER 2016 TRIBOLOGY & LUBRICATION TECHNOLOGY

14 Figure 18 Particulate chemical classification sorting by size obtained from phase 2 gearbox 1. influences caused by operational conditions if oil cleanliness levels are used to evaluate a component s health condition. - have shown that for component deterioration evaluation, more insights may be gained from the bin of size > 14 m and attention should be focused on the overall trends instead of absolute values. - have indicated that transient events causing more component deterioration to wind turbine gearboxes. inline and online oil debris monitoring: - appears to be a better approach for monitoring gearbox condition than using oil cleanliness-level measurements because the latter is more affected by operational conditions and oil age. - can both be effective if used to detect changes in wind turbine gearbox debris generation trends; however, inline sensors may be an easier option in terms of model development when oil debris counts are used to estimate gearbox remaining useful life. - has shown that large debris can be generated during the run-in of wind turbine gearboxes. It warrants the necessity of oil filtration during a gearbox runin and before being put into full service. - has shown that damaged gearboxes have much higher debris generation rates than healthy gearboxes. online oil CM: - has shown that oil temperature and moisture are slow-changing variables and less affected by transient load changes during turbine operations. - has shown that oil quality, mainly determined by oil contamination levels, has no strong correlation with loads experienced by a wind turbine gearbox. offline oil sample and debris analysis: - can reduce the opportunity of producing erroneous results by conducting multiple analyses. - can provide detailed debris composition information to help identify possible root causes and recommend appropriate corrective actions. - has a few benefits if conducted by a dedicated laboratory, such as analysis that is conducted by qualified analysts following certified procedures, and the thresholds for key monitoring parameters are defined based on a wide database. - is important to stay with one technology and focus on the trends, if used to evaluate oil cleanliness levels. In addition, it is important to examine possible outliers and determine the nature of the particle and whether it represents the true condition of the analyzed oil. To make oil and wear debris analysis more beneficial to wind turbine gearbox CM and easily accepted by the wind industry, the following recommendations (based on this study) can provide opportunities for improvement: Make run-in of wind turbine gearboxes and filtration after run-in an industry requirement and recommend that end users check the oil cleanliness level when a wind turbine gearbox is received. Make the use of oil cleanliness-level measurements an industry standard for controlling and monitoring run- 76 Hurricane season is from June to November when the seas are at their warmest and most humid.

15 in of wind turbine gearboxes and investigate whether inline or online oil debris counting sensors can be used to serve the same purpose. Develop new materials or lubricants that can better tolerate the transient loading conditions experienced by wind turbine gearboxes. Research threshold setting methods based on either online or inline oil debris sensors so that appropriate maintenance actions can be recommended. Conduct long-term evaluation of currently available online oil CM sensor techniques, such as oil quality, and develop new effective techniques, if needed. Standardize terminologies on damage modes used by different parties or technologies. Evaluate or develop cost-effective oil and debris analysis techniques that may be portable and can be easily used to make wind turbine gearbox maintenance decisions. Investigate methods to account for small debris (<4 m in size) in wind turbine gearboxes and research their impacts on wind turbine gearbox reliability or oil property. Both the conclusions and the opportunities listed are by no means exhaustive. It is hoped that this study can serve as a bridge between the wind turbine gearbox stakeholders and the Society of Tribologists and Lubrication Engineers or other relevant research communities so some needed work can be done with oil and debris analysis and the related tribological work, such as new materials and lubricants for wind turbine gearboxes. As a result, wind turbine gearbox service life can be extended to benefit the wind industry, including both landbased and offshore applications. INCREASED EFFICIENCY ENERGIZED BY LANXESS delivers effective, economical biocides and fungicides for Preventol, Biochek and Veriguard products are registered, reliable and available Preventol CMK Preservative and Preventol CMK 40 are approved by the FDA, EPA and NSF (HX-1) as antimicrobial preservative for lubricants with incidental food contact. The approved use is up to 1% active ingredient (p-chloro-m-cresol). PREVENTOL is a registered trademark of LANXESS Deutschland GmbH. BIOCHEK and VERIGUARD are registered trademarks of LANXESS Corporation. FUNDING The equipment from Parker Kittiwake, Macom Technologies/Poseidon Systems, and GasTOPS loaned to NREL and the support for conducting oil CM research are sincerely acknowledged. The support from SGS Herguth Laboratories and FEI Aspex is greatly appreciated. The author is also grateful for the support from the GRC dynamometer and field testing teams. This work was supported by the U.S. Department of Energy under Contract No. DEAC36-08GO28308 with the National Renewable Energy Laboratory. Funding for this work was provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind and Water Power Technologies Office. 78 OCTOBER 2016 TRIBOLOGY & LUBRICATION TECHNOLOGY