Search

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]

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]

AbstractBased 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]

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]

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]

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]

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]

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.

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 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 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 aim of this work is to assess the compliance of the estimated lifetime of the planetary gearbox of hydromechanical transmission of a BELAZ mining dump truck with the actual operational one based on the results of accelerated (forced) bench tests. Preliminary strength calculations were carried out for the two most stressed modes of movement of a mining dump truck in a quarry on the rise, where the most significant damage to gear trains takes place: for maximum productivity (power) and maximum torque. Investigations showed that the gears limiting the lifetime of the gearbox are the sun gear and satellites of the 3rd planetary series. As a result of the tests, the estimated operating time of the planetary gearbox on the bench was achieved, which guarantees an operational life of at least 400,000 km of the dump truck run.

Data-driven fault diagnosis methods have been widely used in the field of fault diagnosis of rotating machinery components. However, most of the current research methods mainly rely on a large amount of data generated by fixed-length data segmentation. The segmented data is usually a short-period small segment signal, and the actual long-period redundant signal cannot be directly used as a test sample for fault identification. In view of the above shortcomings, a new fault diagnosis method based on data probability density and one-dimensional convolutional neural network (DPD-1DCNN) is proposed. It has two characteristics: ①the density feature of the extracted signal resists the redundancy of the data; ②adapt redundant signals of different lengths as input to the diagnostic model. The method is verified on the planetary gearbox fault data generated by the DDS test bench, which not only ensures high diagnostic accuracy, but also enhances the adaptability of the diagnostic model.

Aiming at the problem of fault feature extraction and pattern recognition of planetary gearbox, a planetary gearbox fault diagnosis method combined honey badger algorithm(HBA) optimal improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN), hierarchical weighted permutation entropy(HWPE) and grey wolf algorithm(GWO) optimal support vector machine(SVM)was proposed. Firstly, HBA was used to optimize the white noise amplitude weight and noise adding times of ICEEMDAN, and HBA-ICEEMDAN decomposition was performed on the vibration signal of planetary gearbox to obtain several mode functions, and the components with larger correlation coefficients were selected for reconstruction. Then, sensitive feature values of reconstructed low noise signals were extracted by HWPE method to obtain fault feature vectors. Finally, GWO was used to optimize the penalty coefficient and kernel coefficient of SVM, and GWO-SVM multi fault classifier was trained to realize damage identification of planetary gearbox, and the experiments were carried out with the vibration data of planetary gearbox, and the effectiveness of the algorithm was verified. The research results show that the fault diagnosis method of planetary gearbox combining HBA-ICEEMDAN, HWPE and GWO-SVM can accurately identify typical single point faults and composite faults of planetary gearbox, with an identification accuracy rate of 98.15%. Comparing with other combination methods, the method has more advantages and effectiveness in fault diagnosis of planetary gearbox. [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]

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]

Aiming at the problems of low accuracy of fault diagnosis for the planetary gearbox of wind turbine and its multiple sources of vibration excitation, a method used to diagnosis the detect of the planetary gearbox was proposed, it was based on multi-channel fusion and multi-scale dynamic adaptive residual learning（MC-MSDARL）. Firstly, the proposed multi-scale dynamic adaptive convolution neural networks（MSDAC） was used to dynamically adjust the weights of convolution kernels at different scales to adaptively extract the local and global intrinsic features of single channel data. Secondly, in order to improve the learning ability of the model, the method combined MSDAC with residual learning. Finally, MC-MSDAR was used to fuse the multi-scale features of multi-channel data into a feature vector, and then it was inputted to SoftMax layer to achieve the identification and classification of the fault which was in the planetary gearbox. The research results show that the accuracy of fault diagnosis of planetary gearbox based on MC-MSDAR is 97%, which verifies the effectiveness of this method. When the results of MC-MSDAR is compared with the results implemented by other deep learning methods, the proposed MC-MSDAR has a better performance on generalization ability than other deep learning methods. [ABSTRACT FROM AUTHOR]

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 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]

Tooth break is a common fault which always occurs in the planetary gearboxes, and when it occurs, due to the influence of transmission path, the vibration signal collected by the sensor becomes complicated, which brings difficulties to fault diagnosis. Thus, in order to study the influence of transmission path on spectrum structure of planetary gearboxes with tooth break fault, the rigid-flexible coupling models with no fault, sun gear tooth break fault, planet gear tooth break fault and ring gear tooth break fault of planetary gearboxes are established on Adams, and the vibration signals of the box body are extracted and analyzed by simulation. The simulation results show that: in the time-domain, the change of the path will have an obvious amplitude modulation on the vibration signal which is measured at a fixed point of the box. What's more, in the spectrum diagram, the two sides of the meshing frequency will also produce side bands composed of different frequencies.This study can provide basis and reference for fault diagnosis of planetary gearboxes in the future.

In view of the problems of poor discrimination of fault feature vectors extracted in the process of fault diagnosis of planetary gearboxes and insufficient diagnosis success rate, a method based on Local Mean Decomposition（LMD） permutation entropy and BP neural network is proposed. Through the LMD decomposition of the original signal, the PF components containing the main information are obtained, and the permutation entropy values are calculated to construct the feature vector. The extracted feature vectors are used to train the BP neural network and complete the failure pattern recognition test. Taking the EMD permutation entropy method and the non-dimensional analysis method as the comparison groups, the experiment proves that the feature vectors extracted from different working conditions with this method are more distinguishable, and the fault diagnosis effect is better. Moreover, this method shows better comprehensive performance when the number of training groups changes.