Planetary Gearbox Research Articles

Study on the dynamic characteristics of planetary gear transmission mechanism of metal cold rolling mill

Using the parametric modelling feature in Solidworks software, a three-dimensional solid model of a planetary gearbox with linear reciprocating motion of the output shaft was constructed. By conducting theoretical calculations related to the kinematics of the transmission system and combining Adams virtual prototyping simulation technology, a dynamic simulation model of the planetary gear transmission mechanism was established to analyse the motion laws and dynamic characteristics. The simulation results were compared and analysed against the theoretical calculation results, and they showed good agreement and consistency. The study also investigated the impact of counterweights on the inertia forces and moments of the output shaft in the transmission mechanism. The results indicated that adding counterweights effectively reduced the inertia impact caused by inertia forces at the start and end positions of the stroke, as well as the inertia torque caused by changes in angular acceleration during the startup and stopping phases, thereby enhancing the smooth operation of the mechanism. Additionally, different thicknesses of counterweights had varying effects on balancing the inertia forces and moments of the output shaft. The study aimed to find the optimal thickness of counterweights to achieve the best balance effect. Furthermore, the study examined the influence of different speeds of the driving gear on the inertia forces of the output shaft. The results showed that at a driving gear speed of 1500 rpm, the horizontal thrust generated by the output shaft was 63566 N, which could provide the required thrust for the rolling mill operation, and the inertia forces of the output shaft tended to stabilize.

Planetary Gearbox Design and Development using Additive Manufacturing

Modern manufacturing technology is dominated by additive manufacturing due to its simplicity, precision, time and material savings, and flexibility in design. The purpose of this study is to propose and print a 3D model of planetary gear trains since these gear trains are more efficient than conventional gear trains and have many advantages. A planet gear trains must have collinear input and output axes in order to achieve compact space requirements. Using even spacing between the planets in a gear train allows both static and dynamic forces to be balanced. It is also possible for multiple planets to produce high torques. Finally, it is capable of providing a wide range of speeds. The printed model performed exceptionally well when it came to performance, and it can be used for both educational and industrial purposes, exhibiting the ability to withstand a high level of stress. The current paper demonstrated the process of 3D printing and the challenges involved in printing planetary gear trains, as well as possible solutions to these challenges. Finally, tolerances were reported as 0.5 mm on the final CAD model, and 0.15 mm on the last updated results.

RELIABILITY ASSESSMENT OF ELECTROMECHANICAL MODULE WITH ASSOCIATION AMONG RELIABILITY INDICATORS

This paper presents the reliability assessment results for electromechanical modules, including carbon fiber reinforced composite with thermoplastic matrix, aluminium alloy, and steel housing, through the analysis of competing risks, establishing an association among reliability indicators and non-stationary failure flow using the Weibull probability density function. It has been demonstrated that the estimated probabilities of reliability operations and failure are merely approximations and may deviate from the actual values. This analysis correlates with the estimation of mutual competing risks in the operation of planetary gearbox housing constructed from composite materials. It has been confirmed that the estimated probability of failures is asymptotically effective. A comparative analysis illustrates reliance on simulation methods for planetary gearbox housing made of carbon fiber reinforced composite with a thermoplastic matrix, aluminium alloy, and steel, focusing on parameters such as von Mises stress and deformation (deflection). It has been proven that the electromechanical module, including carbon fiber reinforced composite with thermoplastic matrix, aluminium alloy, and steel planetary gearbox housing is the most reliable, with the lowest heat transfer coefficient, but with the lowest strength.

Model of the turboprop engine reduction gear tooth harmonic spectral component

The width of spectral components in rotary machines is determined by the frequency modulation of the carrier from the rotor speed deviation in the stationary operation mode. It was shown that, for the tooth spectral component, it is not enough to take into account this factor only. The analysis of publications and the studies performed show that it is also determined by the effect of many other factors: process-related ones (errors in manufacturing and assembly of gear wheels), operation mode parameters (rpm, temperature, transmitted load); design-related factors (flexibility of drive parts; modification of the tooth face), as well as uneven wear of tooth flanks. Using the planetary gearbox of a turboprop engine as an example, the structure of the tooth spectral component width in function of the analyzed influencing factors was considered. Based on the vibration statistics for eighteen gearboxes in overhauled engines, the choice of a ratio for the spectral component width of a frequency modulated process was justified. This choice showed readings closest to the corresponding experimental data. For the vibration of ten gearboxes with different degrees of tooth flank wear, the corresponding dependence of the width on the wear is presented. A mathematical model for the tooth spectral component width in overhauled and newly manufactured gearboxes as the product of the constant coefficient with the deviation dispersion sum from the considered factors was proposed. The same dependence was also given for gearboxes with wear when adding the dispersion from wear to the dispersion sum. It was demonstrated that, for the presented case of the maximum wear, its fraction in the total width was about 50%, whereas the fraction of the frequency modulation from the operating system of adjusting relative rotor speed constancy in the stationary engine operation mode was equal to half of all the other factors.

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

Discriminative manifold domain adaptation for cross-domain fault diagnosis of rotating machineries

Domain adaptation aims to solve the issue of distribution shift. Manifold learning uses Riemann metrics as nonlinear distance to overcome the limitation of Euclidean metric in domain adaptation. However, it ignores the feature distortion and relationship change caused by the feature mapping framework. Class overlap leads to degradation of classifier performance. Thus, this study proposes a new discriminative manifold domain adaptation (DMDA) framework to solve these problems. DMDA can align manifold structures and domain distributions through discriminative manifold alignment (DMA) and joint distribution alignment, respectively. The DMA method is developed via a new pseudo label voting mechanism, the local fisher discriminant analysis, and the geodesic flow kernel. It can achieve the manifold structure alignment and learn the discriminative features. Moreover, a structural risk minimization classifier is constructed to achieve Laplacian regularization and align the joint distributions of the source domain and target domain. It also implements the fault classification task. The experimental results on bearing and planetary gearbox datasets prove that DMDA has better fault transfer diagnosis performance than the typical domain adaptation methods based on machine learning.

Effects of Macro-Pitting Fault on Dynamic Characteristics of Planetary Gear Train Considering Surface Roughness

The planetary gearbox plays a vital role in a wide range of mechanical power transmission systems, including high-speed trains, wind turbines, vehicles, and aircraft. At the same time, the planetary gear train inside the gearbox is regarded as the most susceptible to failure in the entire transmission system. To analyze the influence of surface roughness on the dynamic characteristics of the planetary gear train, a dynamic modeling method based on fractal theory is proposed. Firstly, the tooth surface contact model was established based on the W-M fractal function, and the time-varying mesh stiffness (TVMS) of the planetary gear train was calculated under healthy and tooth macro-pitting. Then, the lumped-parameter method is introduced to construct a planetary gear train translation-torsion dynamic model that comprehensively considers TVMS and tooth backlash. The vibration acceleration signals of the planetary gear train under different macro-pitting states and surface roughness are simulated and calculated, allowing a quantificative analysis of the influence of surface roughness on system vibration response. Finally, the correctness of the model for the planetary gear train is verified by experiments. The results show that compared with the planetary gear train modeling method based on Hertz contact theory, the root mean squared error of the vibration signal of this work under a macro-pitting fault state is reduced by 8.7%, further improving the reliability of the model.

ANALYSIS OF RESEARCH ON PLANETARY GEARBOXES OF ARMORED VEHICLES (REVIEW ARTICLE)

The work is devoted to the review of modern trends in the research and use of planetary gearboxes of armored vehicles. An analysis of the areas of application of mechanical transmissions was carried out. The advantages and disadvantages of using gear gearboxes as part of mechanical transmissions are described. The design features of the use of gears in transmissions are considered. The requirements for gear gearboxes as part of transmissions are defined. The advantages and disadvantages of using planetary gearboxes and their areas of use are analyzed. The classification of mechanical adjustable gears according to general features, as well as individual ones, is considered. An overview of the fulfillment of the conditions for increasing reliability was carried out, taking into account the design features of stepped gearboxes, which are divided into three groups: simple gearboxes (with fixed shaft axes); planetary gearboxes (with movable shaft axes); combined (combination of a simple and planetary gearbox in one unit). A brief review of modern approaches to planetary gear box modeling, taking into account inertial, elastic, dissipative, transformer and frictional properties, taking into account other characteristic features of the planetary series, is carried out. An analysis of known ways of solving problems of such modeling was carried out. Modern methods of calculating the strength of planetary gearboxes, including those with partial destruction, and corresponding software complexes are analyzed. The need for the development of more advanced mathematical models that adequately describe the stress-strain state in gear engagements has been identified. Keywords: design, planetary gearboxes, mechanical transmission, influence of operating factors, diagnostic parameters, synthesis of kinematic schemes

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

In a high-noise environment and with a limited number of faulty samples, it becomes challenging to extract a sufficient amount of useful fault information, which makes gear fault diagnosis more difficult. To address these issues, this paper proposes a fault diagnosis method for planetary gearboxes based on intrinsic feature extraction and attention mechanism. The method utilizes the complementary ensemble empirical mode decomposition algorithm to perform modal decomposition on the fault vibration signal, obtaining a series of modal components. By comparing and selecting the modal components that contain a significant amount of fault features, they are then transformed into two-dimensional images with time–frequency properties using wavelet transform. Additionally, a neural network model based on attention mechanism and large-scale convolution is proposed. The preprocessed images are inputted into the network for feature extraction. During this process, the large-scale convolution with residual structure maximizes the retention of effective feature information, while the attention network further filters the features. Finally, the selected features are used for fault classification. The model is validated using the gear datasets from Southeast University and the University of Connecticut. A comparison is made with the Pro-MobileNetV3, channel attention and multiscale convolutional neural network, multiscale dynamic adaptive residual network, and CBAM-ResNeXt50 models. It is found that the accuracy reaches 100% before adding Gaussian noise and 99.68% after adding noise, which is significantly higher than that of other models.

ДОСЛІДЖЕННЯ ПЕРЕДАТНИХ ВІДНОШЕНЬ ПЛАНЕТАРНИХ ПЕРЕДАЧ ПІДЙОМНО-ТРАНСПОРТНИХ МАШИН

Lifting and transporting machines (PTM) are one of the important equipment for the mechanization of work in various spheres of the national economy. In addition to the multi-rescue system, the cargo lifting mechanism also includes a winch. Winches with one drum, which have standard parameters - traction force from 3.2 to 125 KN at rope speed from 0.5 to 0.1 m/s, are most often used in PTM. Such winches include a two-stage gear reducer that connects the electric motor with the drum. Such a winch drive scheme is quite large and has considerable weight. One of the areas that will help reduce these indicators is the use of planetary gearboxes. They allow the use of winches in which the electric motor and gearbox are coaxial, and the planetary gearbox itself can be mounted in the drum. Such a drive scheme of the load lifting mechanism allows to reduce the dimensions and weight of the winch. The paper considers the calculation and compares the transmission ratios of planetary gears, which are used and can be used in lifting and transporting machines to reduce the dimensions and weight of cargo lifting mechanisms. Schemes of simple and closed planetary gears, as well as a scheme with three central gears, are given. The scheme of the planetary transmission with free gear wheels is considered. The presence of internal gears and several satellites makes planetary gears compact and light in weight. AII transmission with two internal gears and a free carrier allows to obtain significant transmission ratios with a small number of gears. Schemes of planetary gears with free gears allow you to change the angular speed of the output shaft.

Attention guided multi-wavelet adversarial network for cross domain fault diagnosis

Adversarial transfer learning is an effective method for diagnosing rotating machinery faults. However, it does not extract features completely and underutilizes unlabeled target data. Hence, an attention-guided multi-wavelet adversarial network (AMWAN) based on the hybrid metric strategy of a multi-wavelet convolutional feature extractor is proposed. First, hidden fault features in the input data are extracted via multi-wavelet convolution and an attention mechanism. Subsequently, the extracted features are input into the domain discriminator and label classifier. A gradient reversal layer is introduced to map and mix the source- and target- domain samples for feature extraction. Specifically, the k-nearest neighbor is adopted to construct a label propagation model in the source-domain feature layer to obtain pseudo-labels for the target-domain feature data. This extends the labeling information for the target-domain feature data. A hybrid metric based on the maximum mean discrepancy distance and feature cosine metric strategy is introduced in the feature layer for adversarial training, which can significantly enhance the domain adaptive effect and improve the cross-domain fault diagnosis performance of the AMWAN. Experimental validation based on two sets of planetary gearbox datasets indicate that the AMWAN exhibits outstanding fault diagnosis performance under both constant- and variable-speed and -load conditions, i.e., it achieves an average accuracy rate of up to 96.85 %, thus surpassing other methods.

Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data

Although data-driven methods have been widely used in planetary gearbox fault diagnosis, the difficulty and high cost of manual labeling leads to little labeled training data, which limits the classification performance of traditional data-driven methods. Therefore, the semi-supervised fault diagnosis method with few labeled samples becomes one of the main research directions. Graph attention network (GAT) is distinguished from traditional classification network by using graph structure for fault node information aggregation and feature extraction, which is an effective semi-supervised learning algorithm. This paper uses fast Fourier transform to process the original vibration signal of gearbox and use it as graph nodes, and propose a KNN graph construction method using pooling for fuzzy distance calculation. In addition, this paper improves the distribution of attention weights by introducing dynamic graph attention networks to correct the problem that classical static GATs cannot clearly distinguish the weights of different categories of nodes. Experiments show that the method proposed in this paper can better extract fault features in complex gearbox vibration signals with an accuracy of more than 99% with very few labeled samples, and has better diagnostic performance compared with other graph neural network architectures and traditional classification networks.

Quantitative assessment for tooth crack of the sun gear in a planetary gearbox

The sun gear in a planetary gearbox is prone to generate tooth crack due to its much more severe load conditions than other gears. The cracked sun gear will evolve into distinct tooth breakages and thus may deteriorate the operation status and even cause catastrophic loss of the whole transmission system. To avoid tooth crack induced malfunction of the planetary gearbox, the tooth crack of sun gear should be accurately identified during the early stage. Therefore, a novelty quantitative assessment methodology for tooth crack damage is proposed, which is a mathematical model for crack propagation of the sun gear in planetary gearboxes based on vibration acceleration signal. Firstly, the quantitative relationship between the crack propagation parameters along the gear thickness and width directions and the time-varying mesh stiffness is analytically formulated. The analytical time-varying mesh stiffness formulation is incorporated into a translational–torsional dynamic model to reveal the effects of crack propagation on the vibration signal of internal and external meshing pairs in the planetary gearbox. Secondly, a quantitative mapping function is established by taking the cracked section area as the variable and the residual of the summed envelope spectrum amplitude of the vibration acceleration signal as the assessment index. Finally, a numerical simulation as well as an experimental test is conducted and compared to verify the correctness of the proposed assessment methodology. We believe that the established model and the corresponding index provide a physically intuitive yet mathematically quantitative reference for diagnosing tooth crack in planetary gearboxes.

A dual-kinetic energy harvester operating on the track and wheel of rail deceleration system for self-powered sensors

Alternative energy sources play a crucial role in Outback rail systems, where electrical infrastructure is unavailable for automated power devices used for traffic lights, surveillance cameras, sensors, and other safety equipment at railway stations and intersections. The current study proposes an innovative dual-kinetic energy harvester design to reduce train speed at intersections and before it comes to a halt at the station. Additionally, it harvests energy from both the train track and wheel simultaneously. The proposed device consists of three main units. The first unit utilizes vibrational movement based on a rack pinion. An efficient dual-kinetic energy harvesting system has been developed, utilizing a novel concept that captures the vibration energy generated by the movement of the rail under the influence of the train’s weight. Additionally, as the train passes at high speed, the train wheel’s energy will be transmitted to the top of the device, moving the rack to a depth of 30 mm. A spiral spring is employed to restore the rack to its initial position after the wheel has passed. The tension force of the spring causes the rack to adhere to the track, enabling the transmission of vibrations generated by the track’s movement and releasing energy, causing the motion to be induced during the return stroke. The second component is the transmission unit. The bidirectional rotational motion from the rack pinion is converted into unidirectional rotational motion using three bevel gears, two roller clutches, and a planetary gearbox to enhance the rotations of the generator shaft. The third component is a 1500 W/180 V DC motor for electricity generation. Lastly, the charging circuit was developed to recharge supercharged capacitors. The proposed system was installed before the railway station, and practical tests were conducted, which showed an efficiency of up to 55%.

Transmission and Hysteresis Error of a Compound Planetary Gearbox

Transmission and Hysteresis Error of a Compound Planetary Gearbox

A novel vibro-acoustic fault diagnosis approach of planetary gearbox using intrinsic wavelet integrated GE-EfficientNet

Planetary gearbox operates under complex working conditions involving high speed, heavy load, and corrosion. When the planetary gearbox is in tight spaces, it is difficult to measure its signal by conventional methods. In this case, acoustic sensors can measure signal with the noncontact method. This paper proposes a vibro-acoustic fault diagnosis method with respect to planetary gearbox. The method addresses challenges related to weak vibro-acoustic signal, difficulty in extracting fault features, and low diagnostic accuracy and efficiency. Firstly, vibro-acoustic signal is captured by a unidirectional microphone. Next, intrinsic wavelet analysis extracts intrinsic features of the planetary gears. The band-limited intrinsic mode functions (BLIMFs) of the acoustic signal are obtained by optimized variational mode decomposition, and the BLIMFs are then transformed into time-frequency map features. Finally, these time-frequency map features are utilized as the inputs for Ghost module and Efficient channel attention module (GE)-improved EfficientNet model, namely GE-EfficientNet model, to achieve fault diagnosis of planetary gearbox. The superiority of the proposed method is verified by the experimental results which show that the diagnostic accuracy of GE-EfficientNet reached 100%, and the floating-point operations and parameter numbers are only 5.1 G and 0.4 MB, respectively. The results demonstrate that the proposed vibro-acoustic fault diagnosis method achieves good diagnostic accuracy and efficiency.

Fault feature detection of planet gear in multi-stage planetary gearbox based on angle compensation synchronous averaging

Planetary gearboxes are often used for power transmission in wind turbines. It makes the gear fault detection of planetary gearboxes very useful and necessary. Generally, windowed synchronous averaging (WSA) is a widely used method to detect planet gear failure in planetary gearboxes. However, the WSA is not directly suitable for the multi-stage planetary gearbox since different planetary stages are working together and vibrations from different stages are coupled to each other. The coupled interference is the synchronous interference, which cannot be reduced by the WSA. It makes the faulty planet gear of interest difficult to detect. To address this issue, an angle compensation synchronous averaging (ACSA) scheme is proposed. The equi-angle resampling is applied to the obtained vibration first. Then, the synchronous interference can be constructed by applying the synchronous averaging-based angle compensation strategy, and removed by subtracting it from the resampled vibration. Subsequently, the fast spectral kurtosis is utilized on the residual vibration to obtain the envelope signal. Next, the WSA is further applied to the residual vibration and envelope signal for constructing the synthetic vibration and synthetic envelope signal corresponding to the planet gear of interest. Finally, the narrowband demodulation and envelope analysis are used to detect planet gear failure. Experiments on a two-stage planetary gearbox positively support the ACSA scheme.

Multiphysics and comparative analysis on failure detection in planetary gears

Gearbox monitoring is considered an important scientific focus for predicting preventative maintenance plans. In these systems, mechanical defects such as loading problems, eccentricity and torsional vibrations lead to shaft fatigue and other damage to various other mechanical components. Various methods have been developed to detect and identify the presence of defects in gearboxes. In this paper, we investigate the effect of gearbox faults on the current signal by defining an analytical correlation between the physical presence of the fault and the stator current. The theoretical development is supported by experimental measurements taken on a back-to-back planetary gearbox. Planetary gearbox faults result in the motor’s input torque oscillations, generating amplitude and frequency modulation (AM-FM). These modulations have an effect on stator current signals. The study of the stator current was followed by that of vibration signal and acoustic pressure taken simultaneously under the same operating conditions. This comparative investigation aims to present the differences between different techniques and highlight the efficiency of each.

Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm-Based Constrained Multi-Objective Nonlinear Planetary Gearbox Optimization

The multi-objective optimization (MOO) of a planetary gearbox is a challenging optimization problem, which includes simultaneous minimization of a number of conflicting objectives including gearbox volume, contact ratio, power loss, etc., and at the same time satisfying a number of complex constraints. This paper addresses this complex problem by proposing a modified hybrid algorithm, named Multi-objective Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm (HMOBPSO), which integrates PSO and Particle Swarm Optimization (BOA) algorithms with the aim to improve the performance with respect to the considered problem. The proposed approach solves the non-convex Pareto set and provides vital insights for lowering gear weight and efficiency and avoiding early failure. The experimental analysis employs numerical simulations to determine the Pareto optimal solutions to the formulated MOO problem. The results show that the proposed method offers significant improvements in terms of gearbox size, efficiency, and spacing compared to the conventional methods. In addition, an assessment of the optimization performance of the proposed HMOBPSO algorithm has been conducted by comparing it to other established algorithms across several ZDT and DTLZ benchmark problems, where it demonstrated its effectiveness.

Optimization of planetary gearbox using nature inspired meta-heuristic optimizers

In this study, a detailed research was carried out to solve the two-stage planetary gearbox design optimization problem by using nature-inspired optimization methods. Seven different nature-inspired meta-heuristic optimizers were applied to minimize the volume of the planetary gear. The recent optimizers, Grey Wolf Optimizer, Artificial Bee Colony, Multi-verse Optimizer, Cuckoo Search, Whale Optimization Algorithm and Salp Swarm Algorithm methods were applied to the problem for the first time. The optimal volume values obtained with the seven different optimizers are much better than the best values known in the literature. The best fitness value obtained is 30% better than the best known in the literature.