Wind Turbine Gearbox Research Articles

Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

Design of floating wind turbine gearboxes using Gaidai risk assessment method

Generating renewable energy sources, rather than relying on world’s finite natural resources, becoming now increasingly crucial as world agenda focuses on climate issues. System’s high-dimensionality, along with nonlinear cross-correlations between various floating wind turbine mechanical parts, subjected to environmental wind and wave-induced loads, are not always easily handled by traditional risk assessment approaches, dealing with complex environmental energy systems. Empirical bending moments of drivetrain, have been produced using SIMPACK (Multibody Simulation Method), based on in situ wind loads. The aim of this study was to benchmark novel lifetime assessment approach for green energy systems.

Dynamics model and vibrational response analysis of helical gear-rotor-bearing transmission system

As a core component of wind turbines, wind power gear speed increasers often work in harsh environments, so it is necessary to study the vibration characteristics of gear transmission systems. Taking a wind turbine gearbox high-speed stage helical gear transmission system as the research object, the dynamics model of multi-degree-of-freedom helical gear-rotor-bearing transmission system is established by the lumped parameter method and solved by Runge-Kutta method, taking into account the tooth side clearance of the gear, transmission error, eccentricity and support force of the nonlinear bearing. The effects of input torque, transmission error and tooth side clearance on the vibration characteristic response of the helical gear train were analyzed. The relationship between the trend of amplitude change in each direction of the gear and the trend of change in the major frequency components was analyzed under the conditions of changing internal and external excitation. The results show that torsional vibration dominates in the system. As the input torque increases, the vibration amplitude of the driving and driven gears decreases, and the frequency amplitude decreases significantly at multiple locations. Gear transmission errors and changes in tooth side clearance have significant effects on gear vibration amplitudes, but are sensitive only to the meshing frequency. The trend of gear vibration amplitude caused by the change of external excitation of the gear set is synchronized with the trend of main frequency amplitude in the corresponding direction, while the change of vibration amplitude caused by internal excitation is consistent with the trend of mesh frequency amplitude in the corresponding direction. The results of this paper can pave a certain foundation for the design and fault diagnosis of wind power gearbox transmission system.

Fault diagnosis of wind turbine gearbox under limited labeled data through temporal predictive and similarity contrast learning embedded with self-attention mechanism

Data-driven models for wind turbine (WT) gearbox health monitoring have garnered significant attention. However, these models usually depend on extensive manually labeled data, which is costly and time-consuming to obtain at industrial sites. The model performance is also heavily affected by volatile operating conditions of WT gearbox. To overcome these issues, this paper proposes a self-supervised fault diagnosis method based on temporal predictive and similarity contrast learning (TPSCL) embedded with self-attention mechanism. This method aims to extract latent fault features from unlabeled vibration signals, thereby improving diagnostic performance under limited labeled data and volatile working conditions. Firstly, a data augmentation combination strategy is proposed to improve the variety of input data and enhance the generalization. Furthermore, a temporal predictive and similarity contrastive learning model embedded with self-attention mechanism is proposed to extract representations related to WT gearbox health conditions. In this case, the intrinsic characteristics of unlabeled vibration signals are learned, and the operating environment disturbance can be eliminated. Finally, the fault separability is realized by fine-tuning the model with few labeled data, which can finally achieve accurate fault identification of WT gearbox. Compared with existing methods, the proposed method can fully utilize existing monitoring data and achieve better performance in WT gearbox status monitoring and diagnosis under limited labeled data and variable operating conditions, contributing to WT predictive maintenance and efficiency improvement.

A New Theory of Damage Estimation and Fatigue Life Prediction

There are a considerable number of fatigue damage estimation theories and fatigue life prediction of mechanical components. The most popular one is Palmgren-Minor (P-M) theory. This theory has been used in the standards for selecting the bearing –as a component subject to fatigue loading- and for expecting the bearings lives. In Wind Turbine Gearboxes (WTGs), the bearings were selected to be without maintenance for 20 to 25 years; however, in real service life, the bearing suffer from premature failure within a life span of quite less than the design life (1 to 5 years). A new applicable methodology and a procedure of calculation for damage estimation due to fatigue loading and predicting the life has been suggested and tested. Results of 20 rolling and sliding tests which conducted under severe contact loading are used to test this method. The suggested method depends on calculating the number of operating cycles under a specific contact loading level to an equivalent number of loading cycles under the average loading level. This method depends on the area under the S-N curve without any correction or loading factors and can be used to predict the WTG bearings failure to manage the maintenance because the current life prediction standards have very high percentages of error (> 400%). The reliability of this approach can be further verified by utilizing actual operational data from Supervisory Control and Data Acquisition (SCADA), used for overseeing wind turbine operations. Additional examinations are necessary to confirm the dependability of this novel method.

An Adversarial Single-Domain Generalization Network for Fault Diagnosis of Wind Turbine Gearboxes

In deep learning-based fault diagnosis of the wind turbine gearbox, a commonly faced challenge is the domain shift caused by differing operational conditions. Traditional domain adaptation methods aim to learn transferable features from the source domain and apply them to the target data. However, such methods still require access to target domain data during the training process, which limits their applicability in real-time fault diagnosis. To address this issue, we introduce an adversarial single-domain generalization network (ASDGN). It relies solely on data from a single length of data acquisition in wind turbine fault diagnosis. This novel approach introduces a more flexible and efficient solution to the field of real-time fault diagnosis for wind turbines.

A time–frequency ridge extraction diagnostic method for composite faults of bearing gears in wind turbine gearboxes

The bearing and gear faults in the gearbox interact with each other, and the weak fault characteristics are often masked by the strong fault characteristics, making it difficult to accurately extract complete fault information. To solve this problem, a time–frequency ridge extraction diagnosis method based on multisynchrosqueezing transform (MSST) is proposed. This method utilizes MSST to enhance the compound fault features, especially the gear fault amplitude modulation components. It also utilizes the time–frequency ridge extraction method to separate the gear fault amplitude modulation components and the bearing fault impact pulse components. Additionally, it uses time shifting to substitute data and verifies the independence of the harmonic zero hypothesis to determine the accuracy of the fault components. This method provides a favorable basis for the extraction and identification of compound faults, especially weak faults, in complex dynamic signals of the gearbox. The effectiveness of this method is validated through simulation examples and practical applications.

Study on dynamic performance and optimal design for differential gear train in wind turbine gearbox

The differential gear train is a common component in wind turbine gearboxes. Investigating the dynamic performance of the gear train is crucial for improving its transmission capabilities. Unlike planetary and compound gear train, there has been relatively little research on differential gear train. Additionally, transmission efficiency is a critical performance metric for gear train, while addressing volume constraints remains a significant challenge in gear research. Currently, there is a lack of relevant research on dynamic transmission efficiency and precise volumetric modeling for differential gear train. Therefore, this paper introduces a high power density design approach for the differential gear train, utilizing the analysis of its dynamic performance. Practical application is demonstrated through two examples. In the first example, the system achieved a 26.32 % reduction in power loss, a 35.44 % decrease in volume, and a maximum root mean square reduction of 12.4 % in component vibration acceleration. In the second example, the system achieved a 19.21 % reduction in power loss, a 41.07 % decrease in volume, and a maximum root mean square reduction of 103.4 % in component vibration acceleration. This research establishes a solid foundation for improving dynamic performance, reducing energy consumption, and minimizing volume in helical differential gear train.

Data augmentation on fault diagnosis of wind turbine gearboxes with an enhanced flow-based generative model

Deep learning-based fault diagnosis has been rapidly applied in recent years. However, datasets under the normal condition can be easily obtained rather than the faulty datasets. Consequently, such an imbalanced state will be caused between normal and faulty conditions that will lead to poor fault diagnosis. To this end, data augmentation based on an enhanced flow-based generative model (EFBG) is proposed to mitigate the data imbalance problem. Firstly, the Wavelet packet transform is performed on the time series to enhance the feature extraction. Secondly, a flow-based generative model named Glow (Generative flow with invertible 1 × 1 convolutions) is used to augment the faulty datasets. Finally, a random forest is adopted to perform the fault classification. The proposed method was validated in the experiment of the wind turbine gearboxes. Results show that this proposal can increase the fault diagnosis accuracy from 83.26 % to 88.01 %, which demonstrates that this proposal can be set as a data augmentation tool for the fault diagnosis of wind turbine gearboxes.

A Fault Diagnosis Approach for Wind Turbine Gearbox Based on Ensemble Learning Model and Dung Beetle Optimization Algorithm

The gearbox is one of the crucial components in wind turbines, and its performance degradation would give rise to out-of-order or even damage. To accurately identify the fault of the gearbox, a novel fault diagnosis approach based on the ensemble model and the dung beetle optimization algorithm is introduced. Firstly, an ensemble learning model with different activation functions is established. Secondly, the dung beetle optimization is applied to select the base models for improving the performance and generalization ability of the ensemble model. Finally, the proposed method is tested with a gearbox dataset. The experimental results show that the proposed approach achieves higher diagnostic accuracy on the gearbox dataset.

Wind turbine condition monitoring based on double‐layer ensemble KNN method

AbstractAn ensemble K‐nearest neighbor model based on the double‐layer sampling method was proposed for the condition monitoring of wind turbine (WT) gearboxes. The distance function was improved to affinity distance, which helped overcome the limitation of obtaining local optimums. The feature priority was calculated based on the regularized mutual information, and a double‐layer sampling method combining data and feature sampling was designed. On the basis of the statistical process control technology, the warning threshold was designed and the real‐time residual was analyzed. The condition of the gearbox was monitored by combining the failure rate. Experimental results with real supervisory control and data acquisition (SCADA) data demonstrated that the proposed method improved the estimation accuracy of the model and could realize early fault warnings of a WT gearbox approximately 20 days earlier than the SCADA system.

Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes

The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the single model and the hybrid model) have their limitations in dealing with nonlinear and complex temperature data, making it challenging to achieve high-precision prediction results. In order to tackle this issue, this paper introduces a novel two-phase deep learning network for predicting the temperature of wind turbine gearboxes. In the first phase, a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory (BiLSTM) network are separately trained using the same dataset. The two pre-trained networks are combined and fine-tuned to form the 1DCNN-BiLSTM model for the accurate prediction of gearbox temperatures in the second phase. The proposed model was trained and validated by measured datasets from gearboxes from an existing wind farm. The effectiveness of the model presented was showcased through a comparative analysis with five traditional models, and the result has clearly shown that the proposed model has a great improvement in its prediction accuracy.

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Wind turbines usually operate in mountainous, offshore and other field environments. As the wind speed is constantly changing, the gearboxes of wind turbines operate under variable operating conditions, resulting in a non-stationery and time-varying signal characteristic. For existing time–frequency analysis (TFA) methods, they undergo low time–frequency energy concentration as well as noise interference during the fault feature extraction. IMFogram, a recently proposed time–frequency representation (TFR) method, calculates local frequency and amplitude information of signals based on fast iterative filtering (FIF). Moreover, it is able to remove uncertainty on the time–frequency plane, allowing a long enough time window to reduce the local boundary effects associated with the signal decomposition methods. Therefore, it has potential advantages for TFA. However, the actual performance of IMFogram calculation largely depends on the decomposition effect provided by FIF and the selection of model parameters. With the purpose of obtaining a more distinct and focused TFR of the wind turbine vibration signal, a novel TFA method, namely Adaptive IMFogram (AIMFogram), is proposed in this paper. Firstly, a range of intrinsic mode functions (IMFs) are gained after the decomposition of vibration signals using an improved variational nonlinear chirp mode decomposition, which is treated as the input to AIMFogram to solve the unsatisfied result by traditional FIF under strong noise. Secondly, Renyi entropy-based particle swarm optimization is conducted to perform adaptive parameter optimization of the AIMFogram. Later, using the optimal parameter, the proposed AIMFogram achieves a high-resolution TFR of complex multi-component vibration signals. To verify whether the proposed AIMFogram is effective or not, the method was successfully employed to the fault diagnosis of the rolling bearing in an experimental bench and the gearbox in a 850-kW wind turbine.

Dynamic Condition Adversarial Adaptation for Fault Diagnosis of Wind Turbine Gearbox

While deep learning has found widespread utility in gearbox fault diagnosis, its direct application to wind turbine gearboxes encounters significant hurdles. Disparities in data distribution across a spectrum of operating conditions for wind turbines result in a marked decrease in diagnostic accuracy. In response, this study introduces a tailored dynamic conditional adversarial domain adaptation model for fault diagnosis in wind turbine gearboxes amidst cross-condition scenarios. The model adeptly adjusts the importance of aligning marginal and conditional distributions using distance metric factors. Information entropy parameters are also incorporated to assess individual sample transferability, prioritizing highly transferable samples during domain alignment. The amalgamation of these dynamic factors empowers the approach to maintain stability across varied data distributions. Comprehensive experiments on both gear and bearing data validate the method’s efficacy in cross-condition fault diagnosis. Comparative outcomes demonstrate that, when contrasted with four advanced transfer learning techniques, the dynamic conditional adversarial domain adaptation model attains superior accuracy and stability in multi-transfer tasks, making it notably suitable for diagnosing wind turbine gearbox faults.

A Bayesian deep learning framework for reliable fault diagnosis in wind turbine gearboxes under various operating conditions

Vibration-based fault diagnostics combined with deep learning approaches has promising applications in detecting and diagnosing faults in wind turbine gearboxes. Specifically when time series vibration data is transformed to a 2-dimensional cyclic spectral coherence maps, the accuracy of deep neural networks in classifying faults increases. Nevertheless, standard deep learning techniques are vulnerable to inaccurate predictions when tested with new data originating from unseen faults or unusual operating conditions. To address some of these shortcomings in the context of wind turbine gearboxes, this paper investigates fault diagnostics using Bayesian convolutional neural network which provide accurate results with uncertainty bounds reducing wrong overconfident classifications. The performance of Bayesian and standard neural networks is compared using a simulation-based dataset of acceleration signals generated from a multibody dynamic model of a 5 MW wind turbine. The framework proposed in this paper has relevance to fault detection and diagnosis in other rotating machinery applications.

Cross-device target migration intelligent diagnosis method of wind power gearbox faults

The current convolutional neural network (CNN) deep learning algorithm for wind power gearbox fault diagnosis requires a substantial amount of high-quality tagged data from testbeds to achieve accurate results. However, it may not perform well in practical engineering scenarios where there is limited fault tag data and incomplete fault information. To overcome this challenge, a novel approach utilizing cross-device target transfer for intelligent diagnosis of wind turbine gearbox faults is proposed. This method transforms the fault diagnosis of wind turbine gearboxes into an image recognition and classification task. Initially, the source domain gearbox testbed data is subjected to dimension transformation using the Gramian angular difference field technique, resulting in the generation of small sample two-dimensional images. This transformation simulates the limited fault tag data scenario encountered in practical engineering. Once the fault features have been augmented, the small sample source domain gearbox testbed data is fed into the IConvNeXt deep CNNs. These CNNs are interconnected and specifically designed for the field. They aim to extract and recognize fault features, thereby creating a pre-training model. The pre-training model is then transferred to the target domain wind power gearbox data for fault diagnosis experiments. The results of these experiments demonstrate the significant effectiveness of the proposed method in recognizing faults in small sample wind power gearboxes within the target domain. Overall, this method successfully achieves the goal of intelligent fault diagnosis through target transfer. It addresses the challenge of limited fault tag data and incomplete fault information, thereby enhancing the performance of CNNs in practical engineering scenarios.

Numerical and experimental investigations on thermoelastic hydrodynamic performance of planetary gear sliding bearings in wind turbine gearboxes

The application of planetary gear sliding bearings (PGSBs) in large wind turbine gearboxes (WTGs) has become an important development trend for high-power wind turbines due to the advantages of improving power density and reducing failure rates. However, the working characteristics of operating at heavy-load conditions and using helical gears to mesh will induce large deformation, axial misalignment, and non-negligible temperature rise in PGSBs. In this paper, a comprehensive analysis model taking into account the influence of axial misalignment, thermal effects, and thermoelastic deformation was presented for the PGSB in WTGs. A full-size test rig for PGSBs was built and simulation experiments were conducted to verify the model. Both the measured oil temperature and film thickness agreed well with the numerical results. The predictions from the model were compared with the results of the isothermal-EHD and rigid-THD models. The comparison results indicated that the pad deformation and temperature rise had obvious effects on the bearing performance and could not be ignored. The effects of bearing clearance and aspect ratios on thermoelastic hydrodynamic performance were analyzed. The numerical results showed that both a lower clearance ratio and a higher aspect ratio could help to reduce the maximum pressure at the bearing edges.

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.

Research on the dynamic characteristics of wind turbine gearboxes under the spatiotemporal inhomogeneous in the wake

In response to the problem of increasing fatigue load and frequent faults caused by the wake effect, the dynamic characteristics and fatigue life analysis of wind turbine gearbox under the Spatial-temporal non-uniformity of wake region were carried out in this paper. A rigid-flexible coupling model of gears transmission system for a 2 MW wind turbine was established. Based on the three-dimensional Station-temporal inhomogeneous wake model proposed in our previous work, the wind speed distribution of six typical locations in the wake region was obtained. Results show that, the spatiotemporal inhomogeneous effects of the wake expansion have great impact on the dynamic characteristics of the gearboxes for the downwind turbines located in different positions. Vibration energy representing the magnitude of impact energy during the operation of the gearbox is significantly higher under wake conditions than under upstream conditions; meanwhile, the gearbox vibration signal contains more impact component in the wake. The velocity deficit and increased turbulence intensity results in less intense but more complex vibration non-smoothness in the turbine transmission system. As the downwind distance downwind increases, the vibration frequency distribution of the transmission system at each moment is larger, but the vibration energy is reduced. Under the 3D spatiotemporal inhomogeneous effects of the wake expansion, the downstream turbine with staggered arrangement is less affected by wake and has a smoother vibration than the tandem arrangement. At the same downstream distance, the gear life is better in a tandem arrangement than in a staggered arrangement (a maximum value happens at the downstream distance of 6D). Research in this paper can provide references for the study of the dynamic characteristics of large wind turbine transmission systems under the wake effects as well as for the micro-siting of wind farms.