High Speed Shaft Bearings Research Articles

Experimental Characterization of Common-Mode Voltages and Currents in Gearbox Bearings of a Doubly-Fed Induction Generator Wind Turbine on a Test Bench

Wind turbine (WT) drivetrain failures can lead to expensive repairs and downtime, significantly impacting the Levelized Costs of Electricity (LCOE). Gearbox failures are a major contributor to maintenance costs, with High-Speed-Shaft (HSS) bearings having the highest failure rate. Frequently occurring premature HSS bearing damage indicates design inadequacies. Currently, bearing design standards (DIN/ISO 281) are primarily based on fatigue. Other damage mechanisms like adhesion or damages due to electrical currents passing through the bearings are not considered. WT gearbox bearings are exposed to electrical currents, mainly caused by common-mode (CM) voltages. Bearing currents lead to surface damages and also may promote the formation of white etching areas (WEA) or cracks (WEC) and thus need to be investigated further. The CM voltage is an inherent feature of the power converter, which generates CM currents in the electrical machine. The resulting high voltages at the HSS bearings can lead to electrical discharge currents in the rolling contact, which damage the raceways of the bearing. This paper presents an experimental method for characterization of the currents and voltages that pass from the generator to the HSS of the gearbox in a multi-megawatt WT drivetrain on a test bench. CM currents and voltages measured at the converter are correlated with the shaft voltages at the HSS bearing. The results indicate, that the CM currents directly pass the HSS and its bearings into the gearbox housing. The CM currents show peaks indicating discharge events with over 100 A. Furthermore, at the discharge events, high-frequency oscillations with over 20 MHz are observed in CM current and bearing voltage.

Transfer Learning Strategy for Fault Identification in Wind Turbine High-Speed Shaft Bearing with Limited Samples

Transfer Learning Strategy for Fault Identification in Wind Turbine High-Speed Shaft Bearing with Limited Samples

Effect of generator torque ripple optimization on a geared wind turbine drivetrain

In this paper, the load effect of torque ripple reduction of a wind turbine generator is analyzed on the high-speed shaft gear stage and high-speed shaft bearings, which are the nearest components to the generator. Two generator designs with different torque ripples for the NREL 5‑MW reference wind turbine are considered. A decoupled analysis method is used, where global loads and torque ripple loads are used as input to a multibody model of the drivetrain in order to analyze the gear and bearing load response. Two different multibody models are considered in this work; one traditional decoupled model without generator inertia and one model with additional coupling and generator. For the model without generator inertia, statistical changes are observed for the load case with the largest torque ripple. This subsequently causes a limited increase in gear root bending damage and bearing fatigue damage. For the load case with the smallest torque ripple a limited statistical change and no damage increase is observed. For the model that includes the coupling and the generator, no statistical changes are observed between the simulation with the largest torque ripple and the smallest torque ripple. This is due to the torque ripple load that is expended to overcome the large inertia of the generator.

Loading on a wind turbine high-speed shaft gearbox bearing: Ultrasonic field measurements and predictions from a multi-body simulation

Wind turbine gearbox failures, many of which result from load-driven rolling element bearing failures, account for long downtimes and high maintenance costs. Non-invasive, in-situ measurement of bearing roller loads using ultrasound facilitates better understanding of the load history and failure mechanisms these gearbox bearings experience. In this study, ultrasonic load measurements from an instrumented high-speed shaft bearing were compared against results from a static multibody simulation. Measurements taken under steady operating conditions were found to agree well with the model. After quantification of the error in ultrasonic measurements the model enabled identification of load outliers which were attributed to transient events. Over- and under-loading instances may act to promote a variety of failure mechanisms, leading to premature bearing failure.

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.

Health assessment of wind turbine bearings progressive degradation based on unsupervised machine learning

High-speed shaft bearing (HSSB) failures are exorbitant since they lead electrical energy generation to halt suddenly. In order to identify the health condition of the wind turbine and preserve the sustainability of energy production, a nonlinear vibration-based monitoring technique based on kernel principal component analysis (KPCA) has been developed. After extracting degradation characteristics from the time, frequency, and time-frequency domains. The most sensitive features are then fused using KPCA to capture the monitored bearing’s operating conditions; this method demonstrated its efficiency in dealing with the nonlinearity of the system. To detect flaws in HSSB and assess whether it is healthy, degraded, or broken, [Formula: see text], and SPE charts have been used. Real run-to-failure data from a wind turbine HSSB is used to validate the proposed technique. The suggested strategy caught the nonlinear relationship in the process variables more successfully than existing techniques, including linear PCA, and demonstrated enhanced process monitoring performance.

Simulative Investigation of the Risk of Smearing Damage for a WT Gearbox Roller Bearing during Rotor-Induced Excitations

Wind turbine drivetrains can be subjected to highly dynamic loading conditions caused by grid faults, power converter faults and dynamic wind excitations. These loading conditions can cause additional wear and possibly damage their components. Some of the most critical components in the mechanical drivetrain are its bearings. High-speed shaft bearings are especially prone to failure. Smearing is one possible damage pattern for these bearings. Previous studies observed a highly increased smearing risk caused by generator-induced torque excitations. In contrast, this study focuses on rotor-induced torque excitations and investigates the resulting smearing risk. The goal is to ascertain the general damage potential stemming from rotor-induced excitations for high-speed shaft bearings regarding smearing. To this end, a detailed bearing model was integrated into a validated multibody simulation of a research nacelle which was operated on a test bench. A smearing criterion was used to evaluate the smearing risk. Multiple sinusoidal rotor-induced torque excitations were investigated. The resulting smearing risk is highly dependent on the excitation amplitude and frequency, with higher amplitudes resulting in a greater smearing risk. Regarding frequency, only excitations with frequencies close to the system’s first torsional eigenfrequency result in a significantly increased smearing risk. In general, the determined amplitudes and frequencies of rotor-induced torque excitations, necessary to cause a significant increase in smearing risk, are unlikely to occur in the field and therefore are of lesser importance to the high-speed shaft bearings than generator-induced torque excitations.

Remaining useful life prognosis for wind turbine using a neural network with a long-term prediction

In this paper, an artificial neural network (ANN) is used to predict degradation phenomena occurring in high-speed shaft bearings wind turbine systems, and predict their remaining useful life (RUL). Two different prediction ways are possible. The first is known as short-term prediction, and it involves using measured three data from prior cycles to anticipate degradation in the present cycle. This is a future prediction. The difficulty with short-term prediction is that it is impossible to predict degradation in the future due to a lack of measurement data. Short-term prediction, on the other hand, is accurate because it is based on real measured data and the extrapolation distance is short. The second method is known as long-term prediction, where predicted degradations are used to predict the degradation at a further future time. This paper considers only the long-term prediction. The method was initially tested by using the experimental vibration data provided by the GPMS database, where the RUL was accurately predicted with a very small uncertainty.

Investigation of a converter fault for a DFIG wind turbine and analysis of the resulting gearbox component loads

Abstract Doubly fed induction generators are widely used in wind turbines in the field. The main advantage is the significantly lower cost of the partial scale power converter compared to the full scale power converter. Converter faults in wind turbines lead to significant generator torque excitations that lead to dynamic loads in the drivetrain. Dynamic loads and changing rotational speeds can lead to gearbox damages. The generator torque excitations have the highest influence on the high speed shaft torque due to the coupling to the generator. Gearbox damages occur mainly on the components of the high speed shaft. Thus, in this paper the influence of a converter fault in a wind turbine with doubly fed induction generator on the high speed shaft component damages is investigated. It is shown that the converter fault can induce dynamic torque excitations with a maximum increase to around 2.5 times rated torque. Due to the resulting dynamic gearbox loading the safety against scuffing in the high speed gear stage decreases by maximum 19 percent. The smearing risk in the high speed shaft bearings increases to around 2 times the value during rated power production.

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

The purpose of this study is to highlight approaches for predicting a system’s future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domain

An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples

The wind power industry suffers from unexpectedly high failure rates in wind turbine high-speed shaft bearings (HSSBs). To reduce cost and improve availability, the industry needs an accurate fault prognostic and remaining useful life (RUL) capability. A reliable prognostic allows maintainers to better define when maintenance can be performed, improving availability or allowing for opportunistic maintenance. Wind turbines operate under harsh conditions and condition monitoring data shows the environment to be both non stationary behavior with high noise. This paper proposes a practical and effective data-driven methodology that can be applied for RUL prediction of HSSBs. A new health indicator (HI) is constructed based on the entropy measure of the so called “spectral shape factor” (SSF), after strengthening the original signal by Teager energy operator (TEO). An Elman neural network (ENN) is used for RUL estimation. Furthermore, prediction intervals of the RUL estimates are computed based on the trained ENN model in order to quantify the errors associated with the prediction. The methodology is validated using a real word data collected form 2 MW Suzlon S88 wind turbine.

Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.

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.

Dynamic Degradation Quantification of Wind Turbine High Speed Shaft Bearing Based on Oscillation Based Sparsity Indices

Wind turbine (WT) high speed shaft (HSS) bearing fault is important due to its significant number of failures. However, due to its non-stationary operation under varying speed condition, it is a challenging issue to quantify the degradation of WT-HSS bearing by conventional bearing indices. Aiming at the aforementioned issue, in this paper, tunable Q factor wavelet transform (TQWT) preprocessed sparsity indices are proposed to achieve the dynamic degradation quantification of WT-HSS bearing. Firstly, based on the ability of TQWT in dynamic extraction of fault component of a bearing signal under varying speed condition, low oscillatory transient signal component is separated from a noisy commercial WT-HSS bearing signal by continuously adjustable tunable Q factor wavelet transform (TQWT). Then, considering the suitability of sparsity indices in quantifying extracted fault component based on its energy concentration, four representative sparsity indices namely kurtosis, gini index, negative entropy and reciprocal smoothness index are used to quantify the separated low oscillatory signal component as a measure of WT-HSS bearing health. The proposed indices show a better performance than original sparsity indices and an improved version of sparsity indices based on adaptive weighted signal preprocessing in dynamic degradation quantification of a commercial WT-HSS bearing.

A neural network approach for improved bearing prognostics of wind turbine generators

Condition monitoring of High-Speed Shaft Bearing (HSSB) in Wind Turbine Generators (WTGs) remains a challenging subject for industrial and academic studies. The investigation of mechanical vibration signals presents the most popular method in the literature. Consequently, this work involves a novel data-driven approach for direct HSSB prognosis using the vibration analysis. The proposed method is based on the computation of traditional statistical metrics derived both from the time-domain and frequency-domain via Spectral Kurtosis (SK). Then, the selection of the most suitable features was made using three metrics (monotonicity, trendability, prognosablity) to guarantee a better generalization of the trained Elman Neural Network (ENN). The validation of the proposed method was done using the benchmark of the center for Intelligent Maintenance Systems (IMS) for training and real measured Green Power Monitoring Systems (GPMS) data for testing. We have provided two links for downloading these data sets. The experimental results show that the proposed approach presents a powerful prediction tool. Comparative results with previous work show several advantages for the proposed combination of statistical metrics and ENN, such as the external prediction and real online estimation of the Remaining Useful Life (RUL). Also, some new practical findings are provided in the discussion.

Calculating loads and life-time reduction of wind turbine gearbox and generator bearings due to shaft misalignment

During the last two decades, wind turbine industries have faced high failure rates, downtimes and costly repairs. Gearbox and generator have contributed to this, especially, because their high speed shaft bearings have often failed. In this article, an analytical method is proposed to calculate the reaction loads of flexible connecting couplings installed between wind turbine gearbox and generator. Raction loads are determined from joint kinematics and metal disk pack deformations as well as axial and angular shaft misalignment. The calculations are executed for both flexible connecting couplings and a universal joint shaft and applied to the gearbox high speed shaft. The performance of flexible connecting couplings and universal joint shaft is compared with respect to the bearing loads and life-time of the gearbox high speed shaft. It is shown that the early, unplanned bearing failures of gearbox and generator high speed shaft can often be attributed to the flexible connecting couplings installed between gearbox and generator.

Measurement of roller load, load variation, and lubrication in a wind turbine gearbox high speed shaft bearing in the field

The variability of applied load and the integrity of lubrication are crucial factors dictating the durability of wind turbine gearbox bearings. In this work, new ultrasonic reflectometry techniques have been implemented to measure both load and lubrication in the high-speed shaft bearing of a field operational Vestas V42 wind turbine gearbox.Miniature piezoelectric transducers were bonded onto the bearing outer raceway. The reflected pulses were used to infer bearing load and lubrication. Roller load measurements were seen to vary by 33% across the roller complement, with similar trend observed for reflection coefficient. Inspection of the reflection coefficient patterns were able to show whether the roller inlet region was fully flooded or partially starved, capturing the stochastic behaviour of bearing lubrication.

Adaptive Machine Learning Approach for Fault Prognostics based on Normal Conditions - Application to Shaft Bearings of Wind Turbine

Prognostics can enhance the reliability and availability of industrial systems while reducing unscheduled faults and maintenance cost. In real industrial systems, data collected from the normal operation conditions of system is available, but there is a lack of historical degradation data is often unavailable. Hence, this paper proposes a general data-driven prognostic approach dealing with the lack of degradation data in the offline phase. First, features are computed on the collected raw signal, then One Class Support Vector Machine (OCSVM) is used to detect the degradation, this anomaly detection method is trained using only normal operation data. Then, features are ranked according to the selection criteria. The feature having the highest score is chosen as Health Indicator (HI). Finally an adaptive degradation model is applied for the prediction of the degradation evolution over time and Remaining Useful Life (RUL) estimation. The proposed approach is validated using run-to-failure vibration data collected from a high speed shaft bearings of a commercial wind turbine.

Investigation of dynamic drivetrain behaviour of a wind turbine during a power converter fault

Damage in electrical components during operation of the drive train of wind turbines can lead to highly dynamic load situations. In particular, power converter components are affected by frequent damage, which leads to highly dynamic torque oscillations. As part of a national project, a research nacelle for a wind turbine was set up at the Center for Wind Power Drives (CWD) and examined with the help of a 4 MW test rig. This test rig allows the drive train of a wind turbine to be intensively investigated so that even the highly dynamic load conditions of a power converter fault could be studied. Among other tests, the fault case of a power converter short circuit was synthetically generated. This paper aims to give an insight into the investigation methodology, test setup and looks into the high speed shaft bearing of the gearbox during this load case in detail.

Analysis of Failures of High Speed Shaft Bearing System in a Wind Turbine

During the operation of wind turbines with gearbox of traditional configuration, consisting of one planetary stage and two helical stages high failure rate of high speed shaft bearings is observed. Such a high failures frequency is not reflected in the results of standard calculations of bearing durability. Most probably it can be attributed to atypical failure mechanism. The authors studied problems in 1.5 MW wind turbines of one of Polish wind farms. The analysis showed that the problems of high failure rate are commonly met all over the world and that the statistics for the analysed turbines were very similar. After the study of potential failure mechanism and its potential reasons, modification of the existing bearing system was proposed. Various options, with different bearing types were investigated. Different versions were examined for: expected durability increase, extent of necessary gearbox modifications and possibility to solve existing problems in operation.