Planetary Gearbox Research Articles

Calculation and Validation of Planet Gear Sliding Bearings for a Three-Stage Wind Turbine Gearbox

In recent years, the trend towards larger wind turbines and higher power densities has led to increasing demands on planet gear bearings. The use of sliding bearings instead of rolling bearings in planetary bearings makes it possible to increase the power density with lower component costs and higher reliability. Therefore, the use of planet gear sliding bearings in wind turbine gearboxes has become more common. However, the flexible structure and complex load conditions from the helical tooth meshes lead to highly complex elastic structure deformation that modifies the lubricant film thickness and pressure distribution and, thus, has to be considered in the calculation of the bearing’s load-carrying capacity. This paper introduces a highly time-efficient calculation procedure that is validated with pressure measurement data from a three-stage planetary gearbox for a multi-megawatt wind energy plant. The investigations focus on three main objectives: (i) analyses of experimental and predicted results for different load cases, (ii) validation of the results of planet gear sliding bearing code, and (iii) discussion on mandatory modeling depths for the different planet stages. Results indicate the necessity of further research in this field of applications, particularly for the third-stage bearings.

Kinematic and dynamic analysis of a planetary gear box in steady-state working regime

The paper presents a method for analysis of planetary gear boxes in order to calculate the speed and torque of the epicyclic gear trains elements. Based on these findings, the power flow within the gear box is determined. Additionally, the mechanical efficiency is estimated in order to assess the overall efficiency of the transmission.

Design and research of the plunger pump mechanism for pumping liquid in reservoir pressure maintenance systems

Nowadays, plunger pumps have the most advanced designs. In fact, plunger pumps are the only ones of their kind, capable of operating at high pressures and have the best characteristics. Their use is growing in industry, as they cope with the harsh conditions of pumping liquids of various types. To maintain reservoir pressure, pumps require a high efficiency and reliable design. In order to ensure the operability of the system, it is necessary to select qualitative indicators according to the criteria of optimality for the design of mechanism parts and to achieve an adequate workflow. Thus, the study and determination of the possibility of improving the performance of plunger pumps remains a current task. The results presented in this article include an analysis of the operating conditions of each of the components of the mechanism using a mathematical model of the drive based on the differential equations of motion of the plunger pump, the determination of the corresponding production processes and the production of finite elements. In addition, a kinematic force analysis of the functions of the mechanism under study (positions of analogues of speeds and accelerations of links) and the found motion characteristics is carried out when solving the problem of dynamics of the main shaft of the machine. This article presents a test report to verify the correct functioning of the mechanism in accordance with the initial parameters of the system, the design of a gear planetary gearbox with cylindrical wheels, a cam mechanism pusher and their type of assembly to be performed. This research is carried out using software for engineering mathematics: Mathcad and Python.

Experimental Comparison of Acoustic Characteristics for a High-Efficiency Magnetic Gearbox and a Mechanical Planetary Gearbox for Industrial HVAC Applications

Experimental Comparison of Acoustic Characteristics for a High-Efficiency Magnetic Gearbox and a Mechanical Planetary Gearbox for Industrial HVAC Applications

로봇용 복합 유성기어 감속기 개발과 동력계를 이용한 성능평가

로봇용 복합 유성기어 감속기 개발과 동력계를 이용한 성능평가

Fault severity identification of planetary gearbox based on refined composite multiscale diversity entropy

Planetary gearbox is a key component in modern industry. A sudden failure may cause disastrous consequences. Thus, accurately acquiring the fault severity can be of importance. Diversity entropy emerges as a promising feature extraction tool for monitoring the health condition. However, the original diversity entropy has the defect that the data length of multiple time series will shorten at deep scales, resulting in unstable complexity estimation at high scale. To overcome this defect, a new feature extraction method has been proposed named refined composite multiscale diversity entropy (RCMDE). The proposed RCMDE method combines moving average windows under each scale factor and the refined state probability to improve the statistical reliability, which allows the diversity entropy to explore more refined fault information hidden at deeper scales. The simulation and experiment results proved that the proposed method has the highest diagnostic accuracy with the best stability in fault severity identification of planetary gearbox.

Research on Kinematics and Efficiency Calculation of Binary Logic Planetary Gearbox Based on Graph Theory

In this paper, the graph theory model of the kinematics of the double internal meshing planetary gear is established by splitting the k value of the planetary gear, the system matrix of the dual-state logic planetary gear transmission is assembled, the logic characteristics of the dual-state logic planetary gear transmission control are analyzed, the logic characteristic table of the 32-gear control is established, and the system model of the kinematics analysis and calculation of the entire planetary gear transmission is established. The expressions of rotational speed and transmission ratio of each component (including planetary gear) of the planetary gear in 32 gears are solved and obtained. The efficiency expression of each gear is derived through the Kleinas method, the calculation of the transmission ratio and efficiency of the 32 gears is completed, and the system efficiency diagram is drawn, which provides a reliable basis for the structural design, shift dynamic simulation analysis, and test verification of the dual-state logic planetary transmission.

Investigation of mesh force features under different planet tooth faults in planetary gearboxes

The mesh force of planetary gearboxes is the major excitation source of vibration. Thus, comprehending how gear faults impact the mesh force is essential in identifying specific fault information from the vibration response for accurate fault diagnosis. In this paper, the internal mesh force and mesh stiffness models of the planetary gearbox are established to depict the influence mechanism of planet tooth faults on the internal mesh force. Further, the theoretical internal mesh force features are validated via the ring tooth root strain, where the strain simulation and measurement are carried out, and a strain separation method is developed to obtain the actual mesh force features. The experimental and theoretical results both demonstrate that different planet tooth faults exhibit specific phase and trend features in the internal mesh force, which provides a foundation for tooth fault classification in planetary gearboxes.

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.

Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network

The identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient compound fault samples and single label classification principles. To solve the issue, a capsule neural network with an improved feature extractor, named LTSS-BoW-CapsNet, is proposed for the intelligent recognition of compound fault components. Firstly, a feature extractor is constructed to extract fault feature vectors from raw signals, which is based on local temporal self-similarity coupled with bag-of-words models (LTSS-BoW). Then, a multi-label classifier based on a capsule network (CapsNet) is designed, in which the dynamic routing algorithm and average threshold are adopted. The effectiveness of the proposed LTSS-BoW-CapsNet method is validated by processing three compound fault diagnosis tasks. The experimental results demonstrate that our method can via decoupling effectively identify the multi-fault components of different compound fault patterns. The testing accuracy is more than 97%, which is better than the other four traditional classification models.

Transient Simulation Analysis of Needle Roller Bearing in Oil Jet Lubrication and Planetary Gearbox Lubrication Conditions Based on Computational Fluid Dynamics

The transient lubrication conditions of rolling bearings are different in gearboxes and bearing testers. It has been observed that samples of qualified rolling bearings tested in rolling bearing testers often fail and do not meet lifespan requirements when employed in other lubrication conditions. This may be caused by different factors affecting the bearing in testing and applying lubrication. Needle roller bearings were selected for this study to investigate the causes of this phenomenon in terms of lubrication. Based on the computational fluid dynamics (CFD) method, fluid domain models for the same type of rolling bearings with different lubrication conditions were established. The transient flow fields of rolling bearings with oil jet lubrication in a tester and splash lubrication in a planetary gearbox were simulated. The air–oil transient distribution of rolling bearings in two kinds of lubrication was analyzed. The results indicate that the rotational speed significantly affected the oil jet lubrication of the needle roller bearing. The average oil volume fraction rose by 0.2 with the increase in the bearing speed from 1200 r/min to 6000 r/min and by 0.06 with the increase in the oil jet velocity from 8 m/s to 16 m/s. The splash lubrication of the bearings in the planetary gearbox was directly related to the immersion depth of the rolling bearings in the initial position. Meanwhile, the splash lubrication of the bearings was also affected by other factors, including the initial layout of the planetary gears. The increase in speed from 1200 r/min to 6000 r/min made the average oil volume fraction of splash lubrication decrease by 4.4%. The average oil volume fraction of the bearings with splash lubrication was better than that with oil jet lubrication by an average of 41.9% when the bearing speed was in the low-speed stage, ranging from 1200 r/min to 3600 r/min. On the contrary, the bearings with oil jet lubrication were better than those with splash lubrication by an average of 31.8% when the bearing speed was in the high-speed stage, ranging from 4800 r/min to 6000 r/min.

A lightweight fault diagnosis model for planetary gearbox using domain adaptation and model compression

This article proposes a novel lightweight attention spatiotemporal joint distribution adaptation network fault diagnosis model to address the key challenges of domain transfer and high model complexity in traditional methods. The novelty lies in 1. Using model compression techniques to reduce the complexity of the network model and improve its computational efficiency; 2. Introducing new domain adaptation and adversarial methods to solve the domain transfer problem. The effectiveness of the proposed model is verified through a transfer experiment of planetary gearbox vibration data. The experimental results show that the proposed model reduces the parameters and computational complexity to 18 % and 15 % of the original model, respectively, and has a diagnostic accuracy of over 98 % in cross-condition transfer tasks, and still maintains an accuracy of over 88 % even under high noise levels. This indicates that the proposed model is an efficient and accurate fault diagnosis model.

Feature Extraction of a Planetary Gearbox Based on the KPCA Dual-Kernel Function Optimized by the Swarm Intelligent Fusion Algorithm

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction problems, but the kernel function selection and its blind parameter setting greatly affect the performance of the algorithm. For the optimization of the kernel parameters, it is very urgent to study the theoretical modeling to improve the performance of kernel principal component analysis. Aiming at the deficiency of kernel principal component analysis using the single-kernel function for the nonlinear mapping of feature extraction, a dual-kernel function based on the flexible linear combination of a radial basis kernel function and polynomial kernel function is proposed. In order to increase the scientificity of setting the kernel parameters and the flexible weight coefficient, a mathematical model for dual-kernel parameter optimization was constructed based on a Fisher criterion discriminant analysis. In addition, this paper puts forward a swarm intelligent fusion algorithm to increase this method’s advantages for optimization problems, involving the shuffled frog leaping algorithm combined with particle swarm optimization (SFLA-PSO). The new fusion algorithm was applied to optimize the kernel parameters to improve the performance of KPCA nonlinear mapping. The optimized dual-kernel function KPCA (DKKPCA) was applied to the feature extraction of planetary gear wear damage, and had a good identification effect on the fuzzy damage boundary of the planetary gearbox. The conclusion is that the DKKPCA optimized by the SFLA-PSO swarm intelligent fusion algorithm not only effectively improves the performance of feature extraction, but also enables the adaptive selection of parameters for the dual-kernel function and the adjustment of weights for the basic kernel function through a certain degree of optimization; so, this method has great potential for practical use.

Cross-machine deep subdomain adaptation network for wind turbines fault diagnosis

Recently, subdomain adaptation has gained extensive interest in addressing the problem of wind turbine (WT) fault diagnosis. However, current methods mainly focus on the subdomain adaptation of statistical features and scenarios with constant rotation speed. To overcome these limitations, a new cross-machine deep subdomain adaptation network (CMDSAN) is proposed in this paper for fault diagnosis of WT under multiple operating conditions. CMDSAN contains an improved subdomain adaptive (ISA) mechanism. In ISA, a subdomain distribution shift measure of jointed statistical and geometric features is constructed to boost domain confusion. Meanwhile, to further capture fine-grained information and discriminative features, a local correlation alignment (LCA) strategy is proposed. Additionally, a two-stage training trade-off factor is designed for balancing classification and ISA loss during the training process to improve the transferability of features. Subsequently, test rigs are constructed, i.e., a planetary gearbox test rig and a scaled-down test rig for WT gearbox with a reduction ratio of 110.11, to validate the effectiveness and superiority of CMDSAN. The case studies conducted under constant rotation speed, acceleration, and deceleration demonstrate that the proposed CMDSAN exhibits better fault transfer diagnostic ability than other domain adaptation methods.

Cyclostationary Feature Mode Decomposition and Its Application in Fault Diagnosis of Planetary Gearboxes via Built-In Information

Cyclostationary Feature Mode Decomposition and Its Application in Fault Diagnosis of Planetary Gearboxes via Built-In Information

Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time–frequency representation

As key rotational components of wind turbines, planetary gearboxes, and bearings need special health monitoring and fault diagnosis for reducing downtime and maintenance costs. However, it is still a challenging issue for time–frequency analysis (TFA) techniques to analyze nonstationary and close-spaced fault frequencies of wind turbines. Hence, a high- concentration TFA technique, termed frequency-chirprate synchrosqueezing-based scaling chirplet transform (FCSSCT) is developed. In the FCSSCT, the novel dimension of chirprate is introduced to map the signal into the three-dimensional space of time–frequency-chirprate in contrast to the time–frequency domain in the synchrosqueezing transform (SST), herein, the three-dimensional space of time, frequency and chirprate is calculated based on the scaling-basis chirplet transform (SBCT); then, a frequency-chirprate synchrosqueezing operator (FCSO) is defined in the frequency-chirprate domain to reassign the amplitude coefficients of the SBCT results and a three-dimensional representation of time–frequency-chirprate is obtained; finally, the time–frequency representation (TFR) with concentrated energy is obtained by transforming the three-dimensional representation of the time–frequency-chirprate space into the time–frequency domain. The effectiveness of the developed FCSSCT is verified by the simulated multi-component signals with close-spaced frequencies or crossover frequencies. Experimental analysis results on wind turbine planetary gearbox and bearing show that the developed technique is effective in characterizing nonstationary fault frequencies. Rényi entropies demonstrate that the FCSSCT has much better energy concentration compared with several advanced TFA methods.

Design and Validation of a Pancake Style Planetary Gearbox for an Eddy Current-Based Wearable Gait Training Robot.

Eddy current brakes have been recently used for functional resistance training in individuals with neurological and orthopaedic disorders. These devices consist of a gearbox, a conductive disc, and permanent magnets that can be moved relative to the disc to alter resistance. However, current devices use a commercial planetary gearbox with a tall profile that sticks out from the leg, which affects wearability. This is coupled with the large system inertia, which together impedes potential device transition to clinical and in-home use. In this study, we developed a low-profile, pancake-style planetary gearbox that greatly reduces the protrusion of the device from the leg. We performed a design analysis and optimization to minimize the thickness and inertia of the device while ensuring that it could withstand the maximum expected torque (50 Nm). We then performed human subjects experiments to examine the effectiveness of our new design for functional resistance training. The results indicated that all leg muscles showed a significant increase in activation during resisted conditions. There were also significant after-effects on medial hamstring activation. These results indicate that the new design is a feasible method for functional resistance training and may have a potential clinical value in gait rehabilitation.

Algorithm for synthesis of plane gears according to the constant curvature criterion

This paper presents an algorithm for synthesizing plane gears based on the constant curvature criterion. The derived formulas for pinion gears can be applied in various mechanisms, including clockwork mechanisms, gun turret aiming systems, lifting and transport mechanisms, and planetary gearboxes with tracked propellers. These formulas enable effective analysis of transmission operation and optimal value selection, leading to more rational design and increased efficiency of the entire mechanism. Internal gearing technology allows for significant reductions in drive dimensions and metal consumption compared to traditional involute gears. Improving the technical characteristics of mechanical drives can be achieved by optimizing pinion gear design. This study focuses on the analysis of pinion gears, which reduce profile slippage and transmission wear, resulting in increased efficiency and convenience of operation. Additionally, pinion gears are technologically advanced and reliable. This work develops a mathematical model of a pinion transmission as a flat gearing with a constant gear ratio, providing a foundation for further research and practical applications.

A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD

A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD

Adaptive Intermediate Class-Wise Distribution Alignment: A Universal Domain Adaptation and Generalization Method for Machine Fault Diagnosis.

Many transfer learning methods have been proposed to implement fault transfer diagnosis, and their loss functions are usually composed of task-related losses, distribution distance losses, and correlation regularization losses. The intrinsic parameters and trade-off parameters between losses, however, need to be tuned according to the specific diagnosis tasks; thus, the generalization abilities of these methods in multiple tasks are limited. Besides, the alignment goal of most domain adaptation (DA) mechanisms dynamically changes during the training process, which will result in loss oscillation, slow convergence and poor robustness. To overcome the above-mentioned issues, a novel and simple transfer learning diagnosis method named adaptive intermediate class-wise distribution alignment (AICDA) model is proposed, and it is established via the proposed AICDA mechanism, dynamic intermediate alignment (DIA) adaptive layer and AdaSoftmax loss. The AICDA mechanism develops an adaptive intermediate distribution as the alignment goal of multiple source domains and target domains, and it can simultaneously align the global and class-wise distributions of these domains. The DIA layer is designed to adaptively achieve domain confusion without the distribution distance loss and the correlation regularization loss. Meanwhile, to ensure the classification performance of the AICDA mechanism, AdaSoftmax loss is proposed for boosting the separability of Softmax loss. Finally, in order to evaluate the effectiveness and universality of the AICDA diagnosis model to the most degree, various multisource mixed fault transfer diagnosis tasks of wind turbine planetary gearboxes, including DA and domain generalization (DG), are implemented, and the experimental results indicate that our proposed AICDA model has a higher diagnosis accuracy and a stronger generalization ability than other state-of-the-art transfer learning methods.