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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]

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]

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]

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]

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]

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]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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 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]

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]

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]

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]

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]

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 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]

The conventional deep learning-based fault diagnosis approach faces challenges under the domain shift problem, where the model encounters different working conditions from the ones it was trained on. This challenge is particularly pronounced in the diagnosis of planetary gearboxes due to the complicated vibrations they generate, which can vary significantly based on the system characteristics of the gearbox. To solve this challenge, this paper proposes a robust deep learning-based fault-detection approach for planetary gearboxes by utilizing an enhanced health data map (HDMap). Although there is an HDMap method that visually expresses the vibration signal of the planetary gearbox according to the gear meshing position, it is greatly influenced by machine operating conditions. In this study, domain-specific features from the HDMap are further removed, while the fault-related features are enhanced. Autoencoderbased residual analysis and digital image-processing techniques are employed to address the domain-shift problem. The performance of the proposed method was validated under significant domain-shift problem conditions, as demonstrated by studying two gearbox test rigs with different configurations operated under stationary and non-stationary operating conditions. Validation accuracy was measured in all 12 possible domain-shift scenarios. The proposed method achieved robust fault detection accuracy, outperforming prior methods in most cases. [ABSTRACT FROM AUTHOR]

To address the problem of insufficient real-world data on planetary gearboxes, which makes it difficult to diagnose faults using deep learning methods, it is possible to obtain sufficient simulation fault data through dynamic simulation models and then reduce the difference between simulation data and real data using transfer learning methods, thereby applying diagnostic knowledge from simulation data to real planetary gearboxes. However, the label space of real data may be a subset of the label space of simulation data. In this case, existing transfer learning methods are susceptible to interference from outlier label spaces in simulation data, resulting in mismatching. To address this issue, this paper introduces multiple domain classifiers and a weighted learning scheme on the basis of existing domain adversarial transfer learning methods to evaluate the transferability of simulation data and adaptively measure their contribution to label predictor and domain classifiers, filter the interference of unrelated categories of simulation data, and achieve accurate matching of real data. Finally, partial transfer experiments are conducted to verify the effectiveness of the proposed method, and the experimental results show that the diagnostic accuracy of this method is higher than existing transfer learning methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical & Electrical Engineering is the property of Mechanical & Electrical Engineering Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is a challenging task to detect planetary gearbox fault type under time-varying speeds. Firstly, under time-varying speeds, the fault-induced frequency harmonics of the planetary gearbox are time-varying and close-spaced, and the fault feature is difficult to detect from the spectrum or traditional time-frequency analysis (TFA) results. Secondly, the interference component in the vibration signal resulting from the complicated gearbox configuration is another obstacle for fault detection. As such, the scaling operator demodulation spectrum (SODS) technique with high readability is developed for planetary gearbox fault diagnosis under variable speed conditions. The developed technique consists of two terms: scaling demodulation operator term for transforming all time-varying fault-related frequency harmonics; and spectral selection criterion term for obtaining fault-related frequency harmonics and removing the other smeared frequency components. With the proposed technique, the time-varying and close-spaced fault-induced frequency harmonics can be quantitatively characterized by the spectral peaks. The performance of the SODS-based planetary gearbox fault diagnosis technique is validated by a simulated signal and two experimental signals collected from different test rigs. Furthermore, comparison results with the traditional TFA methods show that the proposed technique has much better ability to identify the fault-related frequency harmonics of the planetary gearbox. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical & Electrical Engineering is the property of Mechanical & Electrical Engineering Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Planetary gearbox plays an important role in many industrial fields and is also a vulnerable component. It is of great significance to develop the time–frequency analysis method of planetary gearbox for ensuring the safe operation of equipment. To analyze the fault characteristics quickly and automatically in time–frequency information, this paper proposes a time–frequency analysis method based on improved U-net++. In the proposed method, the modified U-net++ is used to compress and expand the vibration time–frequency data generated by the normalized S transform, and the original overlap-tile strategy is adjusted. In addition, Tversky loss is introduced into the U-net++ model as an optimization objective. The improved U-net++ is evaluated on the gearbox vibration dataset of in-service wind turbines. The experimental results show that the Dice coefficient of the feature area analysis reached 0.949. The convergence speed and calculation efficiency are also significantly improved, which proved the effectiveness and progressiveness of the improved U-net++ in the planetary gearbox time–frequency analysis. [ABSTRACT FROM AUTHOR]

In the operation and maintenance of planetary gearboxes, the growth of monitoring data is often faster than its analysis and classification. Careful data analysis is generally considered to require more expertise. Rendering the machine learning algorithm able to provide more information, not just the diagnosis conclusion, is promising work. This paper proposes an analysis and diagnosis two-stage framework based on time-frequency information analysis. In the first stage, a U-net model is used for the semantic segmentation of vibration time-frequency spectrum to highlight faulty feature regions. Shape features are then calculated to extract useful information from the segmented image. In the second stage, the decision tree algorithm completes the health state classification of the planetary gearboxes using the input of shape features. The real data of wind turbine planetary gearboxes and augmented data are utilized to verify the proposed framework's effectiveness and superiority. The F1-score of segmentation and the classification accuracy reach 0.942 and 97.4%, respectively, while in the environmental robustness experiment, they reached 0.747 and 83.1%. Equipping the two-stage framework with different analytical methods and diagnostic algorithms can construct flexible diagnostic systems for similar problems in the community. [ABSTRACT FROM AUTHOR]

Purpose: Wear affects the vibration characteristics of gear system by changing tooth thickness. In the planetary gearbox (PGB), the planet gears are prone to a variety of wear types related to the number of planet gears due to their large number and complex motion characteristics. However, it is an unsolved problem to clarify the fault mechanism and fault characteristics of planet gears with multiple wear types. Methods: To study the fault mechanism, this paper proposes a stiffness model based on the three-segment tooth profile equation to calculate the meshing stiffness under health and wear condition. The meshing relative displacement function considering the effect of backlash and wear thickness is established. Considering the effect of wear on the tooth profile error, the meshing error function is established. The meshing stiffness, meshing relative displacement, and meshing error, which are affected by wear, are incorporated into the translation-torsion coupling dynamic model of PGB, and the vibration signals of different wear levels are obtained through the synthetic meshing vibration signal model. To analyze the fault characteristics, the statistical characteristic analysis method based on the residual signal is used to analyze the relationship of statistical indicators and wear types and wear levels. Results and Conclusion: The results show that indicators such as root mean square and frequency kurtosis can be used to evaluate the wear levels, form factor and frequency root mean square can be used to distinguish different wear types, and indicators such as waveform factor and kurtosis are sensitive to initial faults. [ABSTRACT FROM AUTHOR]

A dynamic modeling method for Multistage Planetary Gear Transmission (MPGT) is proposed based on the concept of integral planetary gearbox modeling. The integrated interaction of multiple nonlinear parameters is considered in the dynamic model. The time-varying mesh stiffness of each gear pair is calculated by the energy method. The effects of input torque, gear backlash, and meshing damping on the chaos and impact characteristics of the system are analyzed in detail. The results show that the dynamic behavior of the system is closely related to the Dynamic Meshing Force (DMF). When the system is in the states of chaos, bifurcation, and jumping, the DMF fluctuates violently, and the stability and reliability of the system are seriously affected. With the increase of input torque and meshing damping, the system exits chaos through the inverse period-doubling bifurcation path, which indicates that increasing the input torque and meshing damping can suppress the chaotic motion and enhance the stability of the system. The backlash has a significant effect on the nonlinear behavior and meshing impact characteristics of the system. When the backlash is small, the system is in bilateral impact, and the meshing impact tends to be stable as the backlash increases. In order to improve the vibration characteristics of the system, a slightly larger backlash is necessary. The results can be used to guide the dynamic characteristics design and vibration control of the MPGT. [ABSTRACT FROM AUTHOR]

The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally. [ABSTRACT FROM AUTHOR]

Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of variable multi-scale morphological filtering (VMSMF) and average multi-scale double symbolic dynamic entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal-scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the turn domain resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions. [ABSTRACT FROM AUTHOR]

A planetary gearbox is more complex in structure and motion than a gearbox with a fixed shaft, making it difficult to monitor and make a fault diagnosis in practice. Components must be frequently inspected to avoid excessive wear, but there is no simple way to directly measure wear. The most direct method is to log vibration and temperature signals using external sensors. Wireless sensors offer more space advantages than a wired one, so this study developed a measurement system that features a three-axis MEMS accelerometer, temperature sensing and wireless modules that are integrated into a planetary gearbox. Along with the system, a virtual instrument (VI) utilizing graphics programming language LabVIEW was developed to acquire and display data time and frequency domains to detect the gear's faults. To determine the root cause of vibrations in a planetary gearbox, determine the vibration signal model of amplitude modulation (AM) and frequency modulation (FM) due to gear damage and derive the characteristic frequencies of vibrations for a planetary gearbox, the characteristic frequencies and AMFM modulation were summarized in closed form. Different degrees of each gear damage were then detected in the planetary gearbox. The vibration signal model was validated by experiments to indicate the sideband around the gear meshing frequency and its feasibility for fault diagnosis of a planetary gearbox with the wireless embedded sensor. [ABSTRACT FROM AUTHOR]

Planetary gearboxes are widely used in modern industrial fields due to the characteristics of large transmission ratio and high transmission efficiency, etc. A new diagnosis method is proposed in this work to identify the unknown fault of planetary gearbox under the strong noise background. Firstly, an optimal rank estimation strategy based on signal spectrum clustering results, a prerequisite for using the nonnegative matrix factorization (NMF) method, is proposed to guide the NMF process of original signal spectrum matrix. Secondly, consider the interpretability of decomposition results, the basis matrix generated by NMF is interpreted as the spectrum feature selection library of the original signal. Meanwhile, the basis vector with the largest kurtosis value in the basis matrix is selected to participate in the filtering of the original signal, and the envelope of filtered signal is obtained. Then, the excellent ability of stochastic resonance to amplify weak signals is utilized. A quantitative index independent of the prior fault knowledge is constructed to evaluate the stochastic resonance response of envelope signal. Finally, the fault mode of planetary gearbox is determined by the structural characteristics of resonance response spectrum. Simulation and experimental results confirm the effectiveness and robustness of the proposed method. It has good performance in pattern recognition of the unknown faults of planetary gearbox. [ABSTRACT FROM AUTHOR]

A planetary gearbox with large transmission ratio is a critical component of a wind turbine (WT). When the planetary gearbox suffers from failure, the regular variation of circular pitch vibration energy contains fault pattern information. Although deep learning-based fault diagnosis models have the capability for automatic feature extraction, they rarely consider emphasizing the energy variation characteristics related to the planetary transmission structure in the data representation stage, which degrades the fault diagnosis performance. To solve this problem, this paper describes a novel data representation method, circular pitch cyclic vector (CPCV) and, on this basis, proposes a WT planetary gearbox fault diagnosis method using CPCV and a bidirectional gated recurrent unit (BGRU). Firstly, this paper utilizes root mean square value as the energy evaluation metric for calculating the CPCV. Then, the mapping relationship between circular pitch energy and the ring gear mesh phase is captured using the BGRU through its bidirectional learning capability. Finally, a fully connected layer with softmax is used for fault classification. The proposed method is evaluated by analyzing the in-service WT vibration data collected from a wind farm in eastern China. The experimental results and comparison analysis illustrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A novel fault diagnosis scheme was developed to address the difficulty of feature extraction for planetary gearboxes using refined composite multiscale fluctuation dispersion entropy (RCMFDE) and supervised manifold mapping. The RCMFDE was first utilized in this scheme to fully mine fault features from planetary gearbox signals under multiple scales. Subsequently, as a supervised manifold mapping method, supervised isometric mapping (S-Iso) was applied to decrease the dimensions of the original features and remove redundant information. Lastly, the marine predator algorithm-based support vector machine (MPA-SVM) classifier was employed to achieve intelligent fault diagnosis of planetary gearboxes. The suggested RCMFDE combines the composite coarse-grained construction and refined computing technology, overcoming unstable and invalid entropy in the traditional multiscale fluctuation dispersion entropy. Simulation experiments and fault diagnosis experiments from a real planetary gearbox drive system show that the complexity measure capability and feature extraction effectiveness of the proposed RCMFDE outperform the multiscale fluctuation dispersion entropy (MFDE) and multi-scale permutation entropy (MPE). The S-Iso's visualization results and dimensionality reduction performance are better than principal components analysis (PCA), linear discriminant analysis (LDA), and isometric mapping (Isomap). Moreover, the suggested fault diagnosis scheme has an accuracy rate of 100% in identifying bearing and gear defects in planetary gearboxes. [ABSTRACT FROM AUTHOR]

Existing studies on the dynamic characteristics of planetary gearboxes are generally based on traditional modeling methods. The models were idealized because of various uncertain factors that affect the dynamic characteristics of the planetary gearbox. The influence of non-mechanical components, such as driving motors, on dynamic analysis, is often ignored. Digital twin technology can not only simulate the data characteristics of physical entities to realize the real-time state evaluation and fault diagnosis of complex equipment but also reflect the entity characteristics of the modeled object completely. Therefore, a digital twin multi-body dynamic model of a planetary gearbox is established for detecting the combined fault of the sun gear missing teeth and the planetary gear missing teeth or pitting under different working conditions. The dynamic characteristics of a planetary gearbox subjected to electromechanical coupling were studied. The sun gear fault frequency, planetary gear fault frequency, and both related modulation frequencies appear in the experiment, and the corresponding fault characteristic frequencies also appear in the digital twin simulation. The validity of the proposed model was experimentally verified. Finally, the root-mean-square index was used to analyze the fault sensitivity of the traditional and digital twin models. The results show that the fault diagnosis effect of the digital twin model has a significant advantage, thus verifying the necessity of considering the effect of electromechanical coupling on the dynamic characteristics of the planetary gearbox. [ABSTRACT FROM AUTHOR]

Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large information loss, and does not distinguish the view weight in the fusion process. To this end, a multi-view data level information fusion model CAM_MCFCNN with view weight was proposed based on a channel attention mechanism and convolutional neural network. The model used the channel characteristics to implement multi-view information fusion at the data level stage, which made the fusion position and mode more natural and reduced the loss of information. A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. The proposed method was verified by the data of the planetary gearbox experimental platform. The multi-view data and single-view data were used as the input of the CAM_MCFCNN model and single-channel CNN model respectively for comparison. The average accuracy of CAM_MCFCNN on three constant-speed datasets reached 99.95%, 99.87% and 99.92%, which was an improvement of 0.95%, 2.25%, and 0.04%, compared with the single view with the highest diagnostic accuracy, respectively. When facing limited samples, CAM_MCFCNN had similar performance. Finally, compared with different multi-view information fusion algorithms, CAM_MCFCNN showed better stability and higher accuracy. The experimental results showed that the proposed method had better performance, higher diagnostic accuracy and was more reliable, compared with other methods. [ABSTRACT FROM AUTHOR]

The increasing demand for fossil fuels has led to an increase in their prices; therefore, the regenerative shock absorber (RSA) devices were designed in conjunction with the suspension system industry, in order to reduce the vibration produced whilst driving, which has become a major concern in automobile production. With regards to vehicle development, compared with the current technology, the subject matter of this paper has the following advantages: The current design relates to the RSAs for vehicles, which has an energy recovery function, a simple and new design, a longer life span, a low cost, and are lightweight. A 45° inclined long slot steering mechanism is used in this design to collect the vibration energy generated while driving the vehicle, improving the energy use rate and saving the amount of fuel that is consumed. The reciprocating linear motion in driving the vehicle is converted into a rotational movement. Peak efficiency of 50% and the average efficiency of 45% are demonstrated in the MATLAB system. It converts mechanical energy into electrical energy and restores energy vibration using a suspension shock absorber while driving, which improves energy use and provides greater comfort for passengers while the vehicle is in motion. [ABSTRACT FROM AUTHOR]

Modern wind turbines employ a multistage planetary gearbox to convert the low rotation speed of the turbine blades to the speed required by the generator. Studies have shown that gearbox failures rank the highest among the contributors to an unplanned downtime. Real-time condition monitoring systems can provide useful insights to a turbine's operation there by reducing the chance of an unplanned downtime. This study focused on developing an automated real-time fault detection methodology for a miniature wind turbine planetary gearbox subjected to non-stationary loading. The data-driven multi-component fault detection methodology implements multiple scales of continuous wavelet transform to extract information from a non-stationary signal. This multi-scale approach ensures that all possible component signatures are captured and organized into a feature rich data-set. The wavelet coefficients were then abstracted using descriptive statistics to reduce size of data-set. This was done so as to minimize the computation requirements. The proposed methodology was tested using a pattern recognition algorithm based on Artificial Neural Networks and two Decision Tree algorithms. The results indicated that the proposed methodology worked well with the Decision Tree algorithm thereby ensuring that such a method could be deployed for a compact signal analyzer, where processing capability and memory capacity is premium. Further, a stand-alone application was deployed to automate the process with the trained machine learning model. The proposed method proved its capability in classifying multi-component faults under non-stationary operating conditions. [ABSTRACT FROM AUTHOR]

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This review article summarizes the previously published studies on horizontal axis wind turbine gearbox system and their advantages. From the available literature, the gearbox technology is classified into three major types such as Planetary Gearbox (PG), Continuously Variable Transmission (CVT), and Magnetic Gearbox (MG). Other gearbox types are installed and successfully running under prototype mode. The mechanical gear box types are normally used in large size wind turbines and they have some drawbacks such as high maintenance cost, gearbox failures, less reliable. The mechanical variable ratio gearbox is more appropriate type for the extraction of maximum power with reduce the use of power electronic devices. The outcome of the review suggests that the variable ratio gear box system is more suitable to extract the maximum energy and also recommend for high service loads, and harsh operating conditions. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping, has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate learning process of the DNN architecture is invisible, making it an uninterpretable black-box model. In this paper, a stacked residual multi-attention network (SRMANet) is proposed as a means of feature extraction of vibration signals, and visualizing the model training process, designing Squeeze-excitation residual (SE-Res) blocks to obtain additive features with minimal redundancy and sparsity. This study recommends the use of the attention fusion unit to ensure the interpretability of the model and ultimately to obtain representative features. By feeding the output gradient of the attention layer back to the original signal, the key feature components in the time domain signal can be effectively captured. Finally, the interpretability, identification accuracy and adaptability of the model under different operating conditions are verified on 12 different fault tasks in the planetary gearbox. [ABSTRACT FROM AUTHOR]

It is difficult for classic time-frequency analysis (TFA) methods to characterize close-spaced nonlinear frequency curves with satisfactory time-frequency concentration and adaptively decompose above frequency components. As such, a novel TFA technique, termed synchro-reassigning scaling Chirplet transform (SRSCT) is developed in this paper. The proposed SRSCT is inspired by the scaling-basis Chirplet transform (SBCT) and reassignment theory. The novelties of the SRSCT are concluded as (a) a synchro-reassigning operator (SRO) is constructed for adaptively calculating the ideal time-frequency amplitudes on the instantaneous frequency (IF) ridges from the SBCT result and removing other smeared time-frequency coefficients, as a result, time-frequency resolution of the novel time-frequency representation (TFR) is greatly improved, close-spaced frequency curves are clearly characterized and noise interference is eliminated; and (b) perfect adaptive mode decomposition ability is achieved by combining the SRO, ridge detection method and vold-Kalman filter (VKF). Analysis results of the simulated signal show the effectiveness of the proposed SRSCT. Experimental analysis on faulty planetary gearbox signal shows that the proposed method can clearly characterize fault features. Compared with eight classic TFA methods, the Rényi entropies of the TFRs calculated by the proposed method are smallest in two cases, and they are 2.75 and 3.03, respectively, which means that the SRSCT can generate the TFR with the highest energy concentration. [ABSTRACT FROM AUTHOR]

Accurately and intelligently identifying faults of the planetary gearbox is essential in the safe and reliable operation and maintenance of the mechanical drive system. Recently, fault diagnosis of planetary gearbox has acquired tremendous progress, especially with the rising popularity of deep learning (DL). However, most methods are standard supervised learning where the input is directly mapped to a fault type, and with strong feedback. Also, their learning ways are static and unlike human learning that gradually acquires knowledge by interaction with the environment. To a certain extent, these deficiencies reduce the generalization and intelligence level of DL-based fault diagnosis methods. Besides, due to harsh working conditions, signals acquired often have strong noise and nonlinear features, leading to relatively low accuracy if raw signals are used as the input directly. Thus, this article proposes a new fault diagnosis method based on time-frequency representation and deep reinforcement learning (DRL). We first define fault diagnosis as a sequential decision-making problem in the classification Markov decision process. Next, the vibration signals are converted to uniform-sized TF maps by synchro-extracting transform to enhance the robustness of feature representation. Finally, a diagnosis agent is built and trained in the framework of DRL to learn the optimal classification policy automatically. Experimental results show that this method not only achieves better generalization and stability with an overall accuracy of over 99.5% in single-speed load cases but also outperforms others in multiwork conditions. [ABSTRACT FROM AUTHOR]