Wind Turbine Gearbox Bearings Research Articles

Method for accelerated testing of wind turbine gearbox bearings based on frictional energy in the rolling contact

This paper investigates a method for a novel accelerated test procedure for rolling bearings in wind turbine gearboxes. Established test procedures e. g. highly accelerated lifetime tests (HALT) mainly reflect fatigue damage and not slip-induced damage patterns (e.g. smearing). A commonly used criterion to rate the severity of an operating point regarding slip-induced damage is the frictional energy. Frictional energy is introduced into the rolling contact as soon as there is a significant combination of simultaneously occurring slip and pressure. Up to now only critical thresholds for this criterion that must not be exceeded have been identified on small-scale component test rigs. However, the permissible amount and duration of overshoots of the critical threshold that lead to damage in the actual application are not understood yet. Therefore, the aim is to conduct tests on full-size test rigs in which frictional energy is applied until slip-induced damage occurs. In order to perform these tests in a reasonable time, it is essential to accelerate the test procedures. Thus, this paper introduces a method for accelerated test procedures based on frictional energy in the rolling contact. The requirement is that the same cumulated frictional energy as in field operation is applied in a shorter time on the test rig. A further requirement is that the frictional power in the accelerated test procedure never exceeds the maximum frictional power occurring in the field. This paper shows the theoretical background regarding frictional energy and the transfer to the test procedure.

A surface study of failed planetary wind turbine gearbox Bearings to investigate the causes of the Bearing premature failure issue

The advanced software used in designing Wind Turbine Gearboxes (WTGs) does not solve the premature failure of the gearbox bearings, which still fail within 5%–20% of their design life by flaking. This issue increases the maintenance, downtime, and early replacement costs that limit the investment in the wind energy field. The majority of the previous research have focused on bearing subsurface investigation and the microstructural changes associated with the failure patterns. Conversely, surface investigation can elucidate significant information about the loading levels and contributors to the premature bearing failure. In this study, two bearings from a planetary stage of a failed multi-megawatt wind turbine gearbox underwent surface investigations and analyses. The analyses include indentations, hardness, roughness, and severe damage regions. The study shows that the contact loading stress exceeds the recommended and more than the compressive yield stresses of the bearing materials. The loading distribution on the bearing inner race during the gearbox operation is quite different from the theoretical loading. The transient loadings throughout the service reduce the Wind Turbine Gearbox Bearings (WTGBs) service life. Furthermore, the significant effects of skewing and slipping have been confirmed. Accordingly, the, lubricant filtration system and the design of the planetary stage are recommended to be improved to extend the fatigue life of the wind turbine gearbox bearings.

The influence of lubricants on white etching areas formation in DLC-coated bearing steel under severe boundary lubrication

White etching areas (WEAs) and white etching cracks (WECs) are the catastrophic premature failure modes associated with wind turbine gearbox bearings. Several drivers, including lubricant chemistry, influence the evolution of WEAs and WECs. This study investigated the performance of base oils with various lubricant chemistry against microstructural decay in DLC-coated AISI 52100 bearing steel. Tests were performed in sliding-dynamic loading conditions under severe boundary lubrication. The effect of base oils against WEAs formation in DLC-coated steel is explored by analysing the condition of oils, wear behaviour, and subsurface microstructural change. The study outcomes revealed that the selection of base oils plays a vital role against the WEAs formation in DLC-coated bearing steel subsurface.

An Improved Denoising Method for Fault Vibration Signals of Wind Turbine Gearbox Bearings

Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, a novel joint denoising method for fault WTG-VM signals is proposed in this article, which we have named EWTKC-SVD. First, the empirical wavelet transform (EWT) boundary exploration method is used to optimize frequency band allocation and obtain the multiple intrinsic mode functions (IMFs). Second, the sensitive IMFs are selected according to the calculated correlation coefficient and kurtosis index, avoiding IMF redundancy. Finally, the fault WTG-VM signals are obtained using SVD denoising. Using this approach, the proposed method realizes high-quality denoising of WTG-VM signals. Furthermore, it also effectively solves the existing problems of conventional methods, namely, inefficient IMF selection, high noise, false frequencies, mode mixing, and end effect. Finally, the effectiveness, superiority, and reliability of the proposed method are proved using simulation and practical case results.

Effect of DLC film on the mitigation of white etching areas (WEAs) under dynamic loading

The premature failures in wind turbine gearbox bearings are primarily caused by the decay of subsurface microstructure called white etching areas and white etching cracks. Severe operating conditions of wind turbine gearbox bearings necessitated for tribological coatings to enhance the system performance. The current study investigated the performance of hydrogenated diamond-like‑carbon (DLC) coating against white etching areas formation under sliding-dynamic conditions. The dynamic loading experiments were carried out in the dynamic pin-on-disc tribometer with Polyalphaolefin base stock oil under a boundary lubrication regime. Prior to the dynamic load tests, detailed tribological characterisation of the deposited DLC coating was carried out. The post-test analysis results were compared with the uncoated AISI 52100 bearing steel tribopair to evaluate the performance of DLC-coated bearing steel against white etching areas formation under given conditions. The results obtained show the capability of the proposed coating to delay the formation of white etching areas in the bearing steel.

Influence of MnS inclusion characteristics on generation of white etching cracks in 100Cr6 bearing steel

Wind turbine gearbox (WTGB) bearings suffer from premature failures through white etching cracks (WECs). To investigate causes of this failure mechanism, we tested two types of 100Cr6 bearing steel components. We carried out rolling contact fatigue experiments on standard washers and material extracted from a WTGB bearing. These differed strongly in morphology and orientation of non-metallic inclusions (NMIs) with respect to the raceway, especially of MnS-type. While the standard washer did not exhibit WECs formation, the WTGB bearing did - even under moderate conditions (1.9 GPa Hertzian pressure, 107 cycles). We therefore conclude that MnS-inclusions are a key factor in initiation of WECs in WTGB bearings.

The role of size and volume fraction of carbides on hydrogen embrittlement and white etching areas formation in bearing steel under dynamic loading

Despite of more than two decades of research in white etching areas (WEAs), this phenomenon still persists in wind turbine gearbox bearings leading to a reduction of L10 life by 90 percent. There are various drivers for WEAs formation among which hydrogen is considered one of the root causes which accelerates the microstructural degradation. Diffusible atomic hydrogen reduces the threshold for dislocation motion leading to localized strain accumulation. The tendency to form WEAs in bearing steel depends on the stability of carbide precipitates during plastic deformation and resistance to diffusible hydrogen. In this research work, the role of size and volume fraction of carbides on their relative stability against decomposition and hydrogen embrittlement is studied. The performance of bearing ball samples with different carbide sizes and distribution is tested in the presence of two different lubricants in the dynamic load pin-on-disc (PoD) test rig. The results from the study show that reducing the size of carbide precipitates improved their stability against plastic deformation leading to the stagnation of WEAs formation.

Method for Evaluating the Reliability and Competitive Failure of Wind Turbine Gearbox Bearings Considering the Fault Incubation Point

Aiming to resolve the problem where the reliability of gearbox bearings of wind turbines is easily affected by random impact, this paper puts forward a reliability evaluation method for the competitive failure of gearbox bearings of wind turbines considering the fault incubation point. Firstly, we use Weibull distribution to simulate the fault latent process of wind turbine gearbox bearings. Secondly, a natural degradation model of gearbox bearing is established based on the Wiener process. Then, we model the random impact arrival frequency and impact intensity through a homogeneous Poisson process and normal distribution, respectively. Finally, based on considering the fault incubation point, the natural degradation of the bearing, the instantaneous degradation caused by impact, and the decline in the impact resistance of the bearing, a reliability evaluation model of gearbox bearings of wind turbines is established. A high-speed bearing of a gearbox from a wind farm in northern China is selected for simulation analysis. The results show that the proposed method can better describe the reliability decline process of the gearbox bearings of wind turbines, which has a specific guiding significance for the maintenance of wind turbines.

Digital Twin Based Virtual Sensor for Online Fatigue Damage Monitoring in Offshore Wind Turbine Drivetrains

Abstract In this article a virtual sensor for online load monitoring and subsequent remaining useful life (RUL) assessment of wind turbine gearbox bearings is presented. Utilizing a Digital Twin framework the virtual sensor combines data from readily available sensors of the condition monitoring (CMS) and supervisory control and data acquisition (SCADA) system with a physics-based gearbox model. Different state estimation methods including Kalman filter, Least-square estimator, and a quasi-static approach are employed for load estimation. For RUL assessment the accumulated fatigue damage is calculated with the Palmgren–Miner model. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered intermediate and high-speed shaft bearings show moderate to high correlation (R = 0.50 − 0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15% from measurements.

Investigation of subsurface microcracks causing premature failure in wind turbine gearbox bearings

One of the reasons behind the low rate of expansion of power production driven by wind turbines is the premature failure of their gearbox bearings, which fail within the first quarter of their designed life. The leading causes of fatigue damage in wind turbine gearbox bearings (WTGBs) are not well recognized despite the massive studies in this field. The damage initiates as subsurface microcracks, propagating to a macro scale and reaching the contact surface. For that, studying the microcracks in the early initiation stage (1–15 μm) significantly impacts the bearing damage trigger. This study is based on evaluating the role of non-metallic inclusions and voids on crack initiation, revealing the varying parts of (maximum shear stress, Von-Mises stress, and traction force) in crack initiation. The results indicated that the effect of inclusion is relatively limited on cracking initiation regardless of their sizes, other than voids, which probably have a considerable role in crack initiation and propagation. The three factors (Von-Mises stress, maximum shear stress, and traction force) played significant roles in the initiation of cracks. However, there is an urgent need to improve the design and manufacturing of WTGBs to decrease the formation of voids and carbides and revise the design standards for both (bearing life and contact stress) to consider the actual relative effects of the severe operating events.

Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM

Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art.

Fault Classification of Wind Turbine Gearbox Bearings Based on Convolutional Neural Networks

Gearbox bearings are critical elements of wind power generation systems. Their stable operation supports the power generation, thus reducing the downtime and improving the economic efficiency of wind farms. With the wide availability of sensors, data-driven methods have started to be utilized instead of physical-based methods for condition monitoring of wind energy infrastructures. Deep learning provides significant advantages to achieving this end due to its ability to extract and select representative features without expert knowledge. The present study proposed an intelligent method based on one-dimensional convolutional neural networks (1D-CNN) to extract useful features from the vibration signals and classify different bearing faults. The performance of the proposed 1D-CNN model was evaluated employing the Case Western Reserve University dataset. As a result, the proposed method achieved an average prediction accuracy of 99.56%. The findings confirmed that the method has good stability and potentially be used to reduce operation and maintenance costs.

Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals

Currently, most fault diagnosis methods for wind turbine gearboxes rely on certain unimodal signal, such as vibration or current, which cannot enable reliable and satisfactory performance due to its limited presentation ability. To this end, this paper proposes a new multiview enhanced fault diagnosis framework to learn the correlated and complementary features across current and vibration signals, which are regarded as two different but related views. Multiple statistic features at different wavelet packet decomposition levels are first extracted from raw vibration and current signals, respectively. Then, an unsupervised multiview learning method based on canonical correlation analysis (CCA) is developed to learn maximum correlations between vibration and current features. Finally, the learned enhanced features are used to identify different health conditions. Experimental results show that our proposed method can learn enhanced fault-related features and achieve superior fault diagnosis performance, especially on compound faults, compared with unimodal signal-based methods.

Comparison of Blind Diagnostic Indicators for Condition Monitoring of Wind Turbine Gearbox Bearings

Abstract Under the pressure of climate change, renewable energy gradually replaces fossil fuels and plays nowadays a significant role in energy production. Among different types of energy sources, wind power covered 14% of the EU's electricity demand in 2018. The Operations and Maintenance (O&M) costs of wind turbines may easily reach up to 20–25% of the total leverised cost per kWh produced over the lifetime of the turbine for a new unit. According to Wood Mackenzie Power & Renewables onshore wind farm operators are expected to spend nearly $15 billion on O&M services in 2019. Manufacturers and operators try to reduce O&M on one hand by developing new turbine designs and on the other hand by adopting condition monitoring approaches. One of the most critical and rather complex assemblies of wind turbines is the gearbox. Gearboxes are designed to last till the end of asset's lifetime, according to the IEC 61400-4 standards. On the other hand, a recent study over approximately 350 offshore wind turbines indicated that gearboxes might have to be replaced as early as 6.5 years. Therefore a plethora of sensor types and signal processing methodologies have been proposed in order to accurately detect and diagnose the presence of a fault. Among others, envelope analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the fault. Sometimes the gearbox is equipped with many acceleration sensors and its kinematics is clearly known. In these cases cyclostationary analysis and the corresponding methodologies, i.e., the cyclic spectral correlation and the cyclic spectral coherence, have been proposed as powerful tools. On the other hand often the gearbox is equipped with a limited number of sensors and a simple global diagnostic indicator is demanded, being capable to detect globally various faults of different components. The scope of this paper is the application and comparison of a number of blind global diagnostic indicators which are based on Entropy (permutation entropy, approximate entropy, samples entropy, fuzzy entropy, conditional entropy, and Wiener entropy), on Negentropy (Infogram), on Sparsity (Sparse-L2/L1, Sparse-L1/L0, and Sparse-Gini index) and on Statistics (mean, standard deviation, kurtosis, etc.). The performance of the indicators is evaluated and compared on a wind turbine dataset, consisted of vibration data captured by one accelerometer mounted on six 2.5 MW wind turbines, located in a wind park in northern Sweden, where two different bearing faults have been filed, for one wind turbine, during a period of 46 months. Among the different diagnostic indicators permutation entropy, approximate entropy, samples entropy, fuzzy entropy, conditional entropy, and Wiener entropy achieve the best results detecting blindly the two failure events.

RUL Prediction of Wind Turbine Gearbox Bearings Based on Self-Calibration Temporal Convolutional Network

The prediction of the remaining useful life (RUL) of wind turbine gearbox bearings is critical to avoid catastrophic accidents and minimize downtime. Temporal convolutional network (TCN), as a potential method of RUL prediction, utilizes dilated causal convolution to extract historic information in the time series, by which it can avoid the disadvantage of long-term dependence faced by classical recurrent neural networks (RNNs). However, a large amount of local information is lost after dilated causal convolution, restricting further improvement of accuracy in RUL prediction or even making TCN invalid when the time series data are not sufficient. To address this issue, an improved TCN denoted as self-calibration temporal convolutional network (SCTCN) is proposed for RUL prediction of wind turbine gearbox bearings, in which the dilated causal convolution of TCN is inherited to extract the long-term historic information, and the self-calibration module is used to focus on the local information in the time series. As a result, SCTCN can learn more complete historic information to improve the accuracy of RUL prediction. Bearing RUL prediction experiments on both test bench and wind turbine gearbox are performed to verify the effectiveness of the proposed method, and the experimental results show that SCTCN has higher prediction accuracy compared with other state-of-the-art methods.

Investigation of Dynamic Loads in Wind Turbine Drive Trains Due to Grid and Power Converter Faults

Electrical faults can lead to transient and dynamic excitations of the electromagnetic generator torque in wind turbines. The fast changes in the generator torque lead to load oscillations and rapid changes in the speed of rotation. The combination of dynamic load reversals and changing rotational speeds can be detrimental to gearbox components. This paper shows, via simulation, that the smearing risk increases due to the electrical faults for cylindrical roller bearings on the high speed shaft of a wind turbine research nacelle. A grid fault was examined for the research nacelle with a doubly fed induction generator concept. Furthermore, a converter fault was analyzed for the full size converter concept. Both wind turbine grid connection concepts used the same mechanical drive train. Thus, the mechanical component loading was comparable. During the grid fault, the risk of smearing increased momentarily by a maximum of around 1.8 times. During the converter fault, the risk of smearing increased by around 4.9 times. Subsequently, electrical faults increased the risk of damage to the wind turbine gearbox bearings, especially on the high speed stage.

Mechanical fault diagnosis by using dynamic transfer adversarial learning

Different machine learning approaches have been developed for the fault diagnosis of mechanical systems. To achieve desired diagnosis performance, lots of labeled one-dimensional (1D) signals are required for training machine learning models. however, those signals collected under various working conditions are difficult to be used for both diagnosis model training and testing. For real applications, moreover, the collection of labeled data is more difficult than that of unlabeled ones. To tackle the above challenging points, a dynamic transfer adversarial learning (DTAL) network is proposed for dealing with unsupervised fault diagnosis missions. To this end, an improved feature extractor is developed to deal with 1D mechanical vibration signals. A dynamic adversarial factor is presented to automatically adapt the marginal distribution of the global domain. The conditional distribution of the local domain is employed to make the model independent of training multiple classifiers, so as to reduce the computational burden of the proposed method. The addressed DTAL was evaluated using fault diagnosis experiments for a wind turbine gearbox and benchmark bearings. Compared with other state-of-the-art methods, it has better accuracy and robustness as highlighted by experimental results. The developed model can improve the diagnosis performance under various workloads for mechanical systems.

A proposed criteria to identify wind turbine drivetrain bearing loads that induce roller slip based white-Etching cracks

In this article, the type of roller slip behavior that may result in the formation of white-etching cracks (WECs) in wind turbine gearbox bearings is identified. A new hypothesis based on the inner raceway normal contact load magnitude at the time of roller slip is proposed as the probable cause of WECs. For this purpose, the maximum normal contact loads are identified when roller slip occurs in high-speed shaft bearings at different mean wind speeds. Subsequently, the annual probability of occurrence of the maximum normal loads are obtained. The probability of maximum load under slip exceeding a limit probability is hypothesized as a probable cause for WEC. In order to apply the proposed hypothesis, two different wind turbines high-speed shaft bearings are used: the cylindrical roller bearing of the General Electric 1.5 SLE turbine and the tapered roller bearing of Vestas V52 turbine. Both the chosen bearings are on the generator side of the high-speed shaft. For both turbines, measurement data together with analytical models are used for identifying the slip and the maximum normal contact loads. We propose forecasting the probability of exceedance of a threshold maximum normal contact load level during slip to identify the possibility for inducing WECs.

Detection of Lubrication State in a Field Operational Wind Turbine Gearbox Bearing Using Ultrasonic Reflectometry

Fully flooded lubrication is the ideal state for a rolling bearing; this is especially true in the aggressive environment of a wind turbine transmission where bearings are subject to intermittent operation and highly variable loading. In this paper, a novel ultrasonic reflection method is used to detect the presence of oil between rollers in the bearing. Ultrasonic sensors were instrumented on the static inner (lab) and outer (field) bearing raceways and reflections were captured as the rollers travelled past the sensor. The proportion of the sound wave reflected (known as the reflection coefficient, R) is dependent on the acoustic mismatch of the materials either side of the interface. Changes in R indicate either a steel–air or steel–oil interface as R values transitioned from 1 to 0.95, respectively, and even lower for a steel–roller interface. Consequently, it was possible to detect the presence of lubricant on the raceway between roller passes. From the laboratory measurements, the recurring reflection coefficient patterns between roller passes were used to identify the lubrication condition of the raceway. An absence of these patterns between roller passes indicated the absence of lubricant on the bearing surface. For the field measurements, three bearing lubrication conditions (partial, insufficient, and fully lubricated) were observed. Partially and insufficiently lubricated datasets were found to occur mostly during transient operation. As transient operation is often accompanied by overloading and torque reversals, coupled with the lubrication issues, these all act to increase the risk of premature bearing failure.

The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.