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Wind turbine planetary gearboxes have complex structures and operating environments, which makes it difficult to extract fault features effectively. In addition, it is difficult to achieve efficient fault diagnosis. To improve the efficiency of feature extraction and fault diagnosis, a fault diagnosis method based on improved multivariate variational mode decomposition (IMVMD) and ensemble refined composite multivariate multiscale dispersion entropy (ERCmvMDE) with multi-channel vibration data is proposed. Firstly, the IMVMD is proposed to obtain the optimal parameters of the MVMD, which would make the MVMD more effective. Secondly, the ERCmvMDE is proposed to extract rich and effective feature information. Finally, the fault diagnosis of the planetary gearbox is achieved using the least squares support vector machine (LSSVM) with features consisting of ERCmvMDE. Simulations and experimental studies indicate that the proposed method performs feature extraction well and obtains higher fault diagnosis accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective optimization challenges within mechanical engineering, with a specific emphasis on planetary gearbox optimization. The algorithm is equipped with the ability to dynamically select the optimal mutation operator, contingent upon an adaptive normalized population spacing parameter. The efficacy of HMODESFO has been substantiated through rigorous validation against established industry benchmarks, including a suite of Zitzler-Deb-Thiele (ZDT) and Zeb-Thiele-Laumanns-Zitzler (DTLZ) problems, where it exhibited superior performance. The outcomes underscore the algorithm's markedly enhanced optimization capabilities relative to existing methods, particularly in tackling highly intricate multi-objective planetary gearbox optimization problems. Additionally, the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems, further accentuating its adeptness in resolving complex optimization challenges within this domain. [ABSTRACT FROM AUTHOR]

The optimization of gear pairs in a two-stage planetary gearbox was performed using the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A total of 18 combinations of input parameters were analyzed, varying: gear width (20, 22, 24 mm), module (2.25, 2.5, 2.75 mm) and material (16MnCr5, 34CrNiMo6). Safety factor from pitting (Shu) and safety factor from tooth fracture (Sf) were numerically obtained and those two parameters were chosen as criteria for optimization. TOPSIS identified alternative 1 (20 mm width, 2.25 mm module, 16MnCr5 material) as the optimal solution, demonstrating the highest safety factor values. Additionally, the optimization achieved satisfactory results regarding the mass of gear pairs. After optimization, the mass of gear pairs decreased by 20% compared to the pre-optimization version. [ABSTRACT FROM AUTHOR]

Based on the newly designed planetary gear gearbox with reciprocating linear motion of its output shaft, research on its dynamic characteristics was conducted. The "kinematic mechanism method" was used to calculate the system's transmission ratio, revealing its characteristic of having a relatively large transmission ratio. Combined with the calculation of transmission efficiency, the characteristic of high transmission efficiency was verified. The "velocity vector method" was used to calculate the relationship between the angular velocities of various components of the gearbox. By conducting a stress analysis on the planetary gear transmission system, the forces and torques acting on each component, as well as the meshing forces of the gears were calculated. The "central parameter method" was adopted to simulate the meshing contact between the gears in the planetary gear transmission system as undamped springs. Considering the two translational vibrations along the radial direction and the rotational twisting along the circumferential direction of each component, a translational-torsional coupled dynamic theoretical model of the system was constructed. Dynamic differential equations of the planetary gear transmission system were derived and solved to obtain the support forces and torques of each component at the bearing support positions, enhancing the research on the dynamic characteristics of the planetary gear transmission system. [ABSTRACT FROM AUTHOR]

Gearboxes, known for their compact size and stable transmission capability, are widely used as power transmission structures in various types of mechanical equipment, such as wind turbines, helicopters, and special vehicles. However, due to harsh and non-stationary working conditions, gear surfaces often deteriorate, leading to faults such as wear, pitting, and cracking. Therefore, it is vital to monitor the working status of gearboxes and diagnose gear faults as early as possible. Gear faults can induce characteristic modulation effects near the gear meshing frequency (GMF), resulting in the appearance of fault-induced sidebands in a vibration spectrum. Extraction of these sidebands allows for the diagnosis of gear faults in a gearbox. However, when faced with a planetary gearbox having a complex configuration, strong background noise and additional fault-unrelated sidebands can interfere with fault-induced sidebands, making accurate diagnosis difficult. To address these challenging issues, this paper proposes a novel fault-induced gear meshing modulation sideband extraction method. In the process, the sidebands directly related to the gear fault near the GMF are first identified and then extracted by a variational harmonic mode decomposition (VHMD) method. Accordingly, a fault-related gear meshing modulation component (GMMC) can be accurately reconstructed by summing up the extracted fault-induced sidebands. Using the GMMC, the gear fault severity can be assessed by evaluating its amplitude modulation (AM) effect. The superior performance of the proposed method is finally demonstrated by experimental data. [ABSTRACT FROM AUTHOR]

During the planetary gearbox design process, the issue of light-weighting has been studied using various methods. To get better results from light-weighting, it is necessary for new methods to be considered to deal with this problem. This study proposes the multi-strategy optimized sparrow search algorithm (MSOSSA) that employs five strategies to improve the ability to generate high-quality initial solutions, convergence ability and speed, and the quality of the solution. In the application, the volume of the gearbox is reduced by 6.39%, and the difference in ratio from the previous application is no more than 1.5%. The light-weighting problem of the gearbox is effectively solved. Compared to the other two algorithms, the MSOSSA is six times more likely to produce a high-quality initial solution in a set of 30 runs. The speed of convergence and the ability to converge and generate global optima are the best of the three. [ABSTRACT FROM AUTHOR]

Torsional vibration signal analysis is promising for diagnosing gear faults in planetary gearboxes. However, early-stage fault signatures are relatively-weak and may be ignored or wrongly identified in conventional demodulation analysis, especially under time-varying speed conditions. Two main contributions are made in this paper. Firstly, an analytical model of planetary gearbox torsional vibration signal is established, by jointly considering the gear mesh vibration, torsional resonance, and other time-invariant components. The gear fault characteristics under time-varying speeds are accordingly revealed explicitly. Furthermore, inspired by the unique time-frequency structure of the torsional vibration signal, the multi-band torsional vibration amplitude demodulation method is proposed for gear fault feature extraction. The proposed method combines the gear fault features in multiple characteristic frequency bands in torsional vibration signal, so that the influences from adjacent carrier frequencies, independent frequency harmonics and random noises can be eliminated in amplitude demodulation. As a result, the obtained amplitude demodulated order spectrum outperforms conventional envelope order spectrum and narrow-band amplitude demodulated order spectrum in revealing relatively weak gear fault features. Both numerical simulation and laboratory experiments are conducted, to demonstrate the correctness of the established analytical model and the advantages of the proposed multi-band torsional vibration amplitude demodulation method. Localized fault on the sun, planet and ring gears are successfully diagnosed.

The article considers the creation of a model range of unified electric power drives for commercial electric vehicles. The peculiarity of the work is the realization of the concept of reducing the cost of production of electromechanical power units (EMPU) of electric vehicles by unification of units and components included in its composition. When forming a number of unified electric power drives, the components were subjected to a set of design, layout, technological and economic requirements ensuring the production on their basis of cargo or passenger electric vehicles with gross weight from 3.5 to 12 t, which corresponds to vehicle categories N1, N2, M2, M3. The developed main components of the unified EMPU include: asynchronous three-phase traction electric motors with power range from 80 to 300 kW, torque range from 250 to 1,000 N·m respectively, output shaft rotation frequency of 9,000 rpm and liquid cooling of stator and rotor; inverters designed to control traction motors with input voltage range from 300 to 900 V, output phase current range from 200 to 600 A and liquid-cooled power keys; reduction gearboxes with gear ratio from 3 to 4.5 and step gearboxes with the number of gears from 2 to 4. The result of works on unification of electric power drives for commercial transport also is a number of standard designs of parts and units and assembly units; normative and technical support of control (tests, analysis, measurements) of certification and quality assessment of products; establishment of requirements for technological processes, including for reduction of material, energy and labor intensity to ensure the use of low-waste technologies. When creating the electric truck MAZ-4381EE, corresponding to the vehicle category N2, the EMPU was used as a part of electric motor TAEM 130 with capacity of 130 kW and two-stage shaft gearbox. The created prototype of electric truck is characterized by almost complete localization, providing import independence of most components and intellectual component of the car.

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time–frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level. [ABSTRACT FROM AUTHOR]

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors' knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications. [ABSTRACT FROM AUTHOR]

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Traditional signal processing methods based on acceleration signals can determine whether a fault has occurred in a planetary gearbox. However, acceleration signals are severely affected by interference, causing difficulties in fault identification. This study proposes a gear fault classification method based on root strain and pseudo images. Firstly, fiber optic sensors are employed to directly acquire strain data from the ring gear root. Next, the strain signals are preprocessed using resampling and a time-domain synchronous averaging algorithm. The processed signals are encoded into two-dimensional images using Gramian Angular Fields (GAF). Then, CN-EfficientNet with contrast learning is proposed to analyze and extract deeper fault features from the image texture features. In the classification experiments for different types of faults, the accuracy reached 96.84%. The results indicate that the method can effectively accomplish the task of fault classification in planetary gearboxes. Comparative experiments with other common classification models further indicate the superior performance of the proposed learning model. Visualization based on Grad-CAM provides interpretability for the fault recognition network's results and reveals the underlying mechanism for its excellent classification performance. • A framework for planetary gearbox fault classification is proposed. • A method for measuring the root strain of ring gear tooth is designed. • A pseudo image-based gear strain signal processing algorithm is designed. • CN-EfficientNet network was built to classify pseudo images. [ABSTRACT FROM AUTHOR]

Planetary gearboxes play a crucial role in altering rotary speed and transmitting power in large machines like wind turbines and sophisticated vehicles. There are many nonlinear interfaces, such as splines, bearings, and gear pairs, in planetary gearboxes, and the resulting vibration signal transmission and attenuation mechanisms are still unknown. In this study, a novel method for quantitatively analyzing the transmission and attenuation of vibration signals is proposed. A multibody dynamic model of the planetary gearbox considering nonlinear gear meshing is presented and experimentally validated. To avoid the interference of foundation vibration on the transmission of the fault signal, the fault impact factor (FIF) is used to describe the intensity of the failure, which aligns well with the experimental signals. Based on the FIF, the vibration signal attenuation of nonlinear interfaces such as splines, bearings, and gear meshing interfaces is quantitatively evaluated. To clarify the transfer paths of fault vibration signals inside the gearbox, the transfer path area method (TPAM) based on FIF is proposed. According to the simulated results, the primary transfer paths of fault vibration signals within the gearbox have been identified, which is of great help in understanding the transmission and attenuation of vibration signals in planetary gearboxes. [ABSTRACT FROM AUTHOR]

Recent researches have shown that the multivariable entropy based feature extraction method can obtain better diagnosis results for fault diagnosis of planetary gearbox. However, the nature properties of multivariable entropy have still not been deeply explored: the reliability of multi-source information fusion and cluster consistency for the same fault signal. These two properties will affect the accuracy of fault diagnosis based on multivariate entropy. This paper aims to reveal the nature properties of multivariate entropy. Firstly, a rigid-flexible coupling dynamic model of a planetary gearbox is conducted to establish a pure test environment. Then the generated vibration signals are used to evaluate the fusion reliability and cluster consistency of multivariable entropy. Additionally, a new multivariable entropy feature extraction method called variational embedding refined composite multiscale diversity entropy (veRCMDE) is proposed. Finally, the simulation and experiment results show that high fusion reliability and high cluster consistency enable multivariate entropy to extract more valuable features, and the proposed veRCMDE performs the best in all experiments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Electrical signature–based technique, due to its non-intrusive nature, is very useful for condition monitoring of rotary machines. Major challenge is the dominance of line frequency in the current signature. The present study focused on decreasing this dominance of the line frequency by obtaining the residual signals through autoregressive modeling. The residual signals are used further to extract the health-related features. The recently developed weighted multi-scale fluctuation-based dispersion entropy features are extracted as health indicators. The extracted health indicators are used for classification of different types of planetary gearbox faults. The results reflect that the proposed methodology has the potential for diagnosing different types of planetary gearbox faults with acceptable accuracy values. [ABSTRACT FROM AUTHOR]

As the integral component of various high-end equipment, the dynamic characteristics of planetary gear-motor coupling system (PMCS) is susceptible to the influence of electrical or mechanical subsystems. Especially under complex non-steady state and non-ideal scenarios, the coupling mechanisms between the gear transmission system and the electrical system remain unclear. To address the challenging issue, this study comprehensively takes into account both the internal and external excitations of the mechanical transmission system, and established a planetary gear dynamic model that applicable to analyze the dynamic response under various unsteady conditions; Simultaneously, this study introduces the Rotor Flux Orientation Control (RFOC) algorithm, the Space Vector Pulse Width Modulation (SVPWM), as well as the three-phase asynchronous motor equivalent circuit model and inverter power supply model. Then established the comprehensive drive chain coupling model by combining with the former planetary gear models. The correctness of the model is verified by the simulation and experimental results of the PMCS under steady-state conditions. Thereafter, this study uncovers the vibration-current coupling mechanisms of the gear-motor system in non-steady-state conditions. Furthermore, the dynamic characteristics of the PMCS in non-ideal scenarios are investigated. This work also provides theoretical guidance for condition monitoring of the whole electromechanical systems. [ABSTRACT FROM AUTHOR]

A variety of factors such as time-varying transfer paths, planet shifts, etc., can induce additional modulation sidebands in the spectrum of planetary gearboxes. Most of previous work only considers the effect of a single factor on the sidebands, while these factors may occur at the same time and the corresponding results are not studied. Moreover, although all of these factors can result in additional sidebands, it is unclear that which the most important factor is, and brings difficulty to the fault diagnosis of planetary gearboxes. Aiming at this issue, a universal equation of phenomenological models of planetary gearboxes is developed in this paper to predict the importance of a certain factor in arising modulation sidebands. This model extends the previous phenomenological models which only contain a single factor of sideband formation and can describe the spectral structures of planetary gearboxes with multiple factors considered simultaneously. Effects of multiple factors including time-varying transfer paths, planet shifts and load sharing ratios on the modulation sidebands are analyzed. Furthermore, arbitrary one, two and multiple factors are introduced in the established phenomenological model to make a comparison between these factors. It is found that when the unequal load sharing among planet gears is not severe, a planet shift can result in abundant frequency components more prominently. Finally, the formation mechanisms of modulation sidebands are derived and some experimental validations are carried out to prove the effectiveness of proposed model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The vibration signal of planetary gearboxes under variable speed conditions shows non-stationary characteristics, indicating that fault diagnosis has become more complex and challenging. In order to more accurately diagnose faults in planetary gearboxes under variable speed conditions, a new method is proposed based on the angular domain Gramian angular difference field (ADGADF) and Swin Transformer. This method initially employs the chirplet path pursuit (CPP) algorithm to fit the speed curve of the original time-domain signal and then combines the speed curve with computed order tracking (COT) to achieve equal angle resampling of the time-domain signal, obtaining a stationary signal in the angular domain. On the basis of the above, the angular domain signal is creatively encoded into the two-dimensional images using the Gramian angular field (GAF), which accurately represents the fault characteristics of the original signal. Finally, the Swin Transformer network, with efficient global feature extraction capability, is used to learn advanced features from the images, achieving accurate fault recognition and classification. The proposed method is verified by experiment on the planetary gearbox and its performance is compared with several common coding methods and intelligent diagnosis algorithms. The experimental results show that the proposed method reaches an accuracy of up to 99.8%. In addition, its performance in accuracy, precision, recall, F1-score and the confusion matrix is superior to traditional diagnostic methods. It also offers the advantage of strong robustness [ABSTRACT FROM AUTHOR]

During the planetary gearbox design process, the issue of light-weighting has been studied using various methods. To get better results from light-weighting, it is necessary for new methods to be considered to deal with this problem. This study proposes the multi-strategy optimized sparrow search algorithm (MSOSSA) that employs five strategies to improve the ability to generate high-quality initial solutions, convergence ability and speed, and the quality of the solution. In the application, the volume of the gearbox is reduced by 6.39%, and the difference in ratio from the previous application is no more than 1.5%. The light-weighting problem of the gearbox is effectively solved. Compared to the other two algorithms, the MSOSSA is six times more likely to produce a high-quality initial solution in a set of 30 runs. The speed of convergence and the ability to converge and generate global optima are the best of the three.

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: This study explores the use of acoustic emissions (AE) for detection of ring gear cracks in wind turbine gearboxes (WTG) by means of experimental measurements and signal analysis. Methods: AE measurements were conducted on two full-size WTG, from which one was in a healthy condition and the other one had a crack in one tooth from the ring gear. We analyzed the AE signals using various signal processing techniques, including envelope spectrum, cyclostationarity and wavelet packed decomposition (WPD). Results: No amplitude modulations could be identified using envelope spectrum in the AE signals. Instead, the cyclostationarity and WPD analyses revealed AE energy in higher frequencies that can be related to the crack. Besides, a proposed approach based on AE burst characterization was able to distinguish among normal and fault-related bursts. Conclusion: If appropriate signal processing techniques and specific analysis approaches are employed, the AE analysis has great potential for its application on condition monitoring of WTG. [ABSTRACT FROM AUTHOR]

Planetary gearbox (PGB) usually work in harsh working conditions with low speed and heavy load, and they are prone to wear. Different from the local faults, the distributed faults such as tooth surface wear are often weak and difficult to detect in the early stage, and it is difficult to extract fault characteristic. This paper presents an early fault diagnosis method for the distributed tooth surface wear of PGB to solve this problem. The proposed multi-channel optimal maximum correlation kurtosis deconvolution (MCO_MCKD) algorithm is used to extract fault characteristic. In order to enhance the effect of fault characteristic extraction (FCE), the algorithm first uses the sliding window principle to segment the input signal to establishes multiple channels for maximum correlation kurtosis (max_CK) optimization based on all the short signals obtained. The finite impulse response (FIR) filter with the max_CK is selected to filter the input signal, in order to realize FCE. The influence of tooth wear is mainly reflected in the frequency-domain signal amplitude. In order to realize early fault diagnosis, the frequency-domain statistical indicator fault characteristic energy ratio (FCER) is proposed based on this characteristic. The health status of the equipment is monitored by calculating the FCER of the signal after FCE. Early fault diagnosis is realized based on the mutation of the FCER. The simulation results show that MCO_MCKD algorithm has strong robustness. The experimental results show this proposed method is effective and superior. [ABSTRACT FROM AUTHOR]

The importance of diversified energy production lies in addressing the fuel shortage resulting from high prices, high temperatures, and environmental pollution associated with its production and consumption. Vibrational energy plays a crucial role in generating electrical power. This paper introduces a new concept based on utilizing the vibration forces of chimneys caused by wind and earthquakes. A mechatronic energy-absorbing system was designed, analyzed, and the output power was calculated using SolidWorks and Matlab programs. The design of the Regenerative Damping Chimney (RDC) primarily focuses on converting vibrations into rotational movement of the chimney, which is generated by wind forces. This is achieved by using a metal rope and pulleys to transmit motion to a set of gears. The opposite direction rotation is facilitated by bevel gears and clutches, and a planetary gearbox is employed to increase the rotation of the DC 24 V 400 W generator. The use of a high-watt generator aims to enhance energy production and the damping factor, ensuring the stability of the chimney during storms and vortex winds. The results show the efficiency of 35 % may reach 45 % watts under test to verify that the proposed system is effective and suitable for chimneys and renewable energy applications in factories and companies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]