Presence Of Wind Turbines Research Articles

Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi

Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex patterns in raw sensor data of healthy and faulty bearings. By splitting the data into smaller segments, the model can quickly analyze each segment and generate predictions at high speed. Additionally, simplified algorithms were developed to analyze the segments with minimum latency. The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.06 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence. The model’s ability to generate real-time predictions and to provide an overall assessment of the bearing’s health can significantly reduce maintenance costs and increase the availability and efficiency of wind turbines.

Review of rolling contact fatigue life calculation for oscillating bearings and application-dependent recommendations for use

Abstract. In contrast to the multitude of models in the literature for the calculation of rolling contact fatigue in rotating bearings, literature on oscillating bearings is sparse. This work summarizes the available literature on rolling contact fatigue in oscillating bearings. Publications which present various theoretical models are summarized and discussed. A number of errors and misunderstandings are highlighted, information gaps are filled, and common threads between publications are established. Recommendations are given for using the various models for any oscillating bearing in any industrial application. The applicability of these approaches to pitch and yaw bearings of wind turbines is discussed in detail.

Bearing Fault Diagnosis Based on VMD-WPT-ViT for Wind Turbine

As an important part of wind turbine, the fault diagnosis of rolling bearings is helpful to maintain the stable operation of the fan. However, most of the existing fault diagnosis methods focus on one-dimensional time series data, which is not only difficult to extract multi-level feature information, but also fails to consider the noise problem in the signal. Therefore, to solve the above problems, a noise reduction algorithm combining variational mode decomposition (VMD) and wavelet packet threshold noise reduction (WPT) is proposed, and Vision-Transformer (ViT) model is combined to realize the fault diagnosis of rolling bearings of wind turbines. VMD is used to decompose the original signal and find out the noisy component. WPT is used to de-noise the component, and the de-noised component and other components are reconstructed to form a pure signal and converted into a two-dimensional image. Finally, a diagnostic model is built based on Vision-transformer. The experimental results show that the proposed method can effectively improve the diagnostic accuracy by extracting the advanced global features of the signal.

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

This study presents a digital twin (DT) based wind turbine bearing fault diagnosis approach to address the issues of insufficient fault sample size and inaccurate diagnosis. To assist in diagnosing bearing faults in wind turbines, a DT system was built. Bearing vibration signal enhancement processing, which is based on the Hilbert-Huang transform, is used to improve the data samples of vibration signals and decrease the noise in these signals. In order to diagnose bearing defects in wind turbines, a convolutional neural network model was trained and tested using data-enhanced samples. The experimental results showed that the suggested method is feasible and effective, increased the stability and accuracy of defect diagnosis in wind turbine bearings, and solved the problem of data augmentation in one-dimensional vibration signals.

Study on Condition Monitoring of Pitch Bearings Based on Stress Measurement

Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This relationship allows the identification of cracks on the outer race and an assessment of their severity based on the value of the maximum surface stress. Second, the outer race of the pitch bearing is divided into several segments, and a singularity detection technique is employed to locate the position of cracks on the outer race based on the stresses measured from the segments. To verify the proposed method, a wind turbine pitch bearing test rig was developed in a laboratory. Experimental results have shown that the proposed method can effectively and accurately identify and locate cracks on the outer race of the bearing, thereby demonstrating its great potential as a reliable approach for monitoring the condition of wind turbine pitch bearings.

Research on the lubrication performance of circular tilting pad thrust bearings for large wind turbines

AbstractIn view of the large temperature rise and impact load of thrust bearings in the main shaft system of large wind turbines, this article takes the circular tilting pad thrust bearings in large wind turbines as the research object, and analyses the basic theory of bearing lubrication. The lubrication performance calculation model established for this bearing includes the Reynolds equation, energy equation, film thickness equation, and elastic deformation equation. Theoretically analysing and calculating the lubrication performance of the circular tilting pad thrust bearing, the key performance parameters have identified such as minimum film thickness (hmin), temperature rise (ΔT), power consumption (W), and flow rate (Q). The calculation results show that the eccentricity ratio has a significant impact on the lubrication performance of the circular tilting pad thrust bearing. When both radial and circumferential eccentricity ratios are around 0.5, the bearing exhibits a high temperature rise, leading to a potential risk of bearing burnout. The results also show that at a radial eccentricity ratio of approximately 0.54, the minimum film thickness reaches its maximum value of 10.34 μm. Similarly, at a circumferential eccentricity ratio of about 0.60, the minimum film thickness reaches its peak value of 21.89 μm. This indicates that the eccentricity ratio plays a crucial role in the lubrication performance. In addition, the lubrication performance of the bearing under varying loads has been calculated at a speed of 9.5 r/min. The results demonstrated that the applied load significantly impacts the thrust bearing's performance. The findings elucidate the critical role of considering the eccentricity ratio and operational external load during the bearing design process. This study validates the potential of replacing rolling bearings with sliding bearings in wind turbine main shafts. It also provides a theoretical reference for the future design of sliding bearings.

Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data

Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

With the significant penetration of wind generation, wind turbines require higher and higher lubrication performance for bearings. To improve the lubrication performance of wind power bearings, this study takes wind power bearings as the research object and comprehensively analyzes the wear forms of wind power bearings as well as intelligent lubrication methods. Firstly, the main roles and wear forms of wind turbine bearings are sorted out and analyzed. Secondly, the common lubrication problems of wind power bearings are analyzed from the bearing grease selection, lubrication mode, and lubrication status, highlighting the important influence of lubrication on bearings. Thirdly, the wind turbine bearing wisdom lubrication method research and organization, mainly including the wind power generation bearing lubrication materials, lubrication devices and monitoring methods, and other issues of research and analysis. Finally, current challenges and future development directions are summarized, which are designed to provide theoretical reference and technical support for the related research and engineering practice in the field of wind power engineering.

Wear test programs for roller-type pitch bearings of wind turbines

Abstract. Pitch bearings are critical for the safe and efficient operation of wind turbines. They connect the rotor blades to the rotor hub and allow for pitching movements that control loads and rotor speeds. While four-point-contact ball bearings have been dominant in the past, three-row roller bearings are increasingly used in current designs due to their higher load capacity at the same diameter. Wear of the raceways is one of the possible damage mechanisms in pitch bearings. As roller bearings differ significantly from previous designs and because the operational conditions of wind turbines differ from other industrial applications, it is a reasonable de-risking exercise to undergo wear tests prior to the commissioning of such bearings. This study outlines a process for developing a wear test program based on aero-elastic simulation data and wind speed measurements. The process is then applied to an example roller bearing. The final program covers both standstill conditions and pitch cycles. The first is the main addition to former approaches. With existing test rigs and a reasonable budget and timeline, the program can be executed.

Electrostatic discharge impacts on the main shaft bearings of wind turbines

Abstract. This paper studies the electrostatic discharge effect in wind turbines, a possible trigger source of the main bearing current. A lab setup with a charge generator and downsized wind turbine was built to verify the impact of electrostatic discharge on the main bearing current. In the test, a fatal amplitude for the bearing current was found at only −93 mV driven voltage on the shaft. Compared with the bearing current driven by the common-mode voltage, the electrostatic discharge effect triggers the bearing breakdown at a lower shaft voltage but much higher bearing current amplitude. The results demonstrate that the electrostatic discharge effect is a pattern of the bearing current in wind turbines and is much more dangerous to the bearing.

Complementary Ensemble Empirical Mode Decomposition and Maximum Correlated Kurtosis Deconvolution for Wind Turbine Bearing Fault Feature Extraction

Wind turbine bearing fault signals exhibit characteristics such as nonlinearity, non-stationarity, and susceptibility to external noise interference, making it challenging to extract fault features and identify them accurately. In light of these issues, this paper proposes a fault signal feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD) and maximum correlated kurtosis deconvolution (MCKD). CEEMD is utilized to decompose the signals, reducing mode mixing and eliminating residual auxiliary noise in the decomposition process, thereby obtaining linear and stationary signals that enhance the significance of the features. MCKD is employed to increase the kurtosis value of the fault signals, facilitating the extraction of continuous transient impacts from weak fault signals. Finally, a Convolutional Neural Network (CNN) is employed to validate that the method enhances fault characteristics and improves the accuracy of fault signal recognition.

Using acoustic emission for condition monitoring of the main shaft bearings in 4-point suspension wind turbine drivetrains

ABSTRACT The continuous growth of wind power technology makes condition monitoring of wind turbine components crucially important for their operational efficiency. The main shaft bearings in wind turbines have been identified as one of the most critical components in the system, especially with the ongoing increase in rotor size and weight. This increase made the 4-point suspension drivetrain more preferable. In this study, we present a novel approach for condition monitoring of the main shaft bearings in a 2 Megawatt wind turbine with 4-point suspension drivetrain using primarily acoustic emission (AE). The focus was on the analysis of time and frequency domains of the AE signal, where the dominant frequency of each AE hit was identified and plotted back in the time domain to create the so-called dominant frequency map in specific time intervals for each bearing. A comparison between the two dominant frequency maps of the two bearings gives valuable insights into the condition of the two bearings. The distinctive nature of the dominant frequency bands in the dominant frequency maps presented promising potential for this method. The presented method is straightforward and can be automated and then integrated into a planned predictive maintenance programme for this wind turbine.

Time-dependent reliability-based design optimization of main shaft bearings in wind turbines involving mixed-integer variables

Main shaft bearings are essential transmission components in wind turbines. The failure of the main shaft bearings is inevitably accompanied by catastrophic consequences. In this regard, this paper presents a time-dependent reliability-based design optimization (TRBDO) approach for main shaft bearings involving mixed-integer variables. The maximization of fatigue life and the elastohydrodynamic film thickness under the load spectrum are selected as optimization objectives. The time-dependent reliability and geometric correlation are determined as nonlinear constraints. An efficient two-stage enrichment strategy is introduced to handle the time-dependent probabilistic constraint with mixed-integer design variables. A mixed-integer nonlinear optimization method is developed based on a meta-heuristic algorithm to solve the formulated TRBDO problem. Eventually, the effectiveness and robustness of the proposed approach are demonstrated by a real application in a 5 MW wind turbine.

Modeling of Wind Turbine Interactions and Wind Farm Losses Using the Velocity-Dependent Actuator Disc Model

This paper analyzes the interaction of wind turbines and losses in wind farms using computational fluid dynamics (CFD). The mathematical model used consisted of three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations, while the presence of wind turbines in the flow was simulated as additional source terms. The novelty of the research is the definition of the source term as a velocity-dependent actuator disc model (ADM). This allowed for modeling the operation of a wind farm consisting of real wind turbines, characterized by power coefficients Cp and thrust force coefficients CT, which are a function of atmospheric wind speed. The calculations presented used a real 5 MW Gamesa turbine. Two different turbine spacings, 5D and 10D, where D is the diameter of the turbine, and two different locations corresponding to the offshore and onshore conditions were examined. The proposed model can be used to analyze wind farm losses not only in terms of the geometric distribution of individual turbines but also in terms of a specific type of wind turbine and in the entire wind speed spectrum.

Data‐driven models for predicting remaining useful life of high‐speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm

AbstractWind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data‐driven models to predict the RUL of high‐speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions.

The contributions regarding to the behavior of bearings in wind turbines

With the growth of the wind industry in the world and the commissioning of turbines with powers higher than 1 MW, higher loads and increased stresses have a strong effect on the durability of the bearings that equip the main shaft and the kinematic transmission of wind turbine gears. As a result, the wind industry is demanding greater durability of the bearings that equip the main shaft and the kinematic transmission with gears, and manufacturers are striving to come up with solutions for these requirements. The novelty in this work consists in the identification of some solutions, as well as improvements of the existing solutions for the construction of some mechanical systems for the existing wind turbines. We present below some constructive bearing solutions that are effective in the wind turbine industry. The research in the paper consists of: stress analysis of the load- bearing structure for the axial radial bearing with straight rollers with single ring, some aspects about lubrication of blade bearing in wind turbine and defects in blade bearings.

Fatigue life prediction of rolling bearings based on damage accumulation considering residual stresses

The determination of the fatigue life of rolling bearings is recommended to be conducted using the standard calculation models defined in ISO 281. These models yield reliable results for rotating applications. The standard models are continuously refined to align with advancements in bearing production processes and material improvements. However, when dealing with rolling bearings exposed to complex load conditions including oscillatory movements, as seen in rotor blade bearings in wind turbines, a validated calculation model has yet to be established. In the domain of structural mechanics, fatigue life evaluation for steel components is based on S-N curves. Variable operational loads are treated as load collective, and the load cycle to failure N under given operational conditions is determined through the application of the linear damage rule. This paper introduces a novel model that integrates the linear damage rule with established conventional bearing theories. Within one internal stress cycle, all rolling contacts are regarded as an internal load collective. To evaluate the internal load collective, the stress state at each rolling contact is assessed based on the S-N curve determined in alternating torsional loads. The core rationale behind the selection of an S-N curve lies in the stress criterion τo based on Lundberg-Palmgren theory, which is incorporated in the new model. This paper includes a comprehensive step-by-step procedure for applying the new model, utilizing the cylindrical roller bearing NU 1006 as a reference. The results of this study indicate a favorable agreement between the fatigue life obtained using the new model and those determined according to ISO 281. Furthermore, a new approach is introduced to analytically account for residual stress - es when measured values are available. In this study, by applying this new approach, the results were observed to be in close agreement with the test results. Based on this investigation, it can be confirmed that the new model has the potential to provide reliable results without necessitating the bearing life exponent as well as the correction factors often required in conventional calculation models.

Evaluation of Wear Models for the Wear Calculation of Journal Bearings for Planetary Gears in Wind Turbines

To increase the power density of the electromechanical drive train of wind turbines, journal bearings can be used as planetary gear bearings instead of rolling bearings. This technological change presents new challenges. For example, wind turbine drive systems are subject to dynamic and low-speed operating conditions, which can lead to accelerated abrasive wear of the journal bearings. In addition, oil supply failure or peak loads due to wind gusts and grid and power converter faults could potentially result in catastrophic failure due to adhesive wear in a very short time. Such operating characteristics are, therefore, critical regarding the journal bearing wear lifetime and must be considered in the design. The successful implementation of journal bearings in wind turbines depends on a reliable estimation of adhesive and abrasive wear. In this paper, five different models for the wear calculation of journal bearings are evaluated regarding their suitability for the wear calculation of planetary gear bearings in wind turbines. For this purpose, the following evaluation criteria were defined: parameter uncertainty, parametrization effort, in particular number of parameters, parameterization method, load case dependency of parameters and calculation effort. In order to be able to evaluate the wear models, the wear models are numerically implemented, and the wear of a test journal bearing is exemplarily calculated under load conditions, which are comparable to load conditions in a wind turbine. Relevant influences from the wind turbine system, such as lubricant, material and manufacturing-dependent surface influences, such as roughness and hardness, are considered. The wear models are evaluated with respect to their fulfillment of the defined criteria. The resulting evaluation allows the selection of a wear model that can be used to calculate the wear of planetary gear journal bearings in wind turbines, considering the available input variables.

Crack propagation analysis of slewing bearings in wind turbines applying a modified sub-model technology

As slewing bearings in wind turbines are exposed to complex operational environments, they are prone to crack initiation, propagation and evolution that can lead to contact fatigue problems. Reliable crack extension prediction is a critical requirement. With the goal of obtaining more accurate crack characteristics, this study proposes a crack initiated contact finite element model (CI-CFEM) targeted at the raceway of slewing bearings by introducing a modified sub-model. In the application of the global finite element model (G-FEM) to the slewing bearing system, the rolling displacements of the ball and the boundary displacements are employed in CI-CFEM to take account of external ring deformations and the support conditions. The crack is embedded using FRANC3D, and the stress intensity factor distribution and propagation characteristics of the crack front are investigated. Furthermore, the effect of external load and the design parameters of the slewing bearing on crack propagation are explored, providing insight for optimizing the design of slewing bearings to extend service life. The results reveal that changing external loads and bearing parameters are an effective means of retarding crack propagation rate, while have relatively minor influence on crack growth angle.

A Wind Turbine Bearing Fault Detection Method Based on Improved CEEMDAN and AR-MEDA

A Wind Turbine Bearing Fault Detection Method Based on Improved CEEMDAN and AR-MEDA