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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 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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

• A novel method is proposed for diagnosing faults in planetary gearboxes. • The proposed method can effectively remove noise interference components in TFR. • The results of time-frequency representation have a prominent concentration of energy. • The method can extract the time-varying closely-spaced instantaneous frequency accurately. • No prior knowledge is required to achieve adaptive decomposition of signals. Instantaneous frequency (IF) time-varying closely spaced and severe background noise can introduce fake components into the time-frequency representation (TFR) of most time-frequency analysis (TFA) methods, especially for the vibration signals of variable-speed planetary gearboxes. These fake components lead to a decrease in the readability of the TFR, making it difficult to extract the true IF. To address this issue, a new TFA method called Synchronous Match-Reassigning Transform (SMRT) is proposed. Firstly, a polynomial chirp rate with a single parameter adjustment is constructed to achieve precise matching of the IF. Secondly, a frequency reassignment operator is constructed for the fine refinement of the IF ridge of Synchronous Match-Reassigning Operator (SMRO). Finally, SMRO is combined with VKF to achieve adaptive reconstruction of the true IF and to remove fake components. The TFR of SMRT has high readability and low energy leakage. The superiority and effectiveness of this method have been verified through synthetic signals and actual signals. [ABSTRACT FROM AUTHOR]

Multi-scale noise tuning parameter-induced stochastic resonance method (MNTPSR-1/f β) is proposed for planetary gearbox diagnosis. This method is divided into two steps. The first step is called multi-scale noise tuning (MNT) and the second step called parameter-induced stochastic resonance (PSR). MNT based on wavelet packet transformation tunes the noise to an approximate 1/f β noise which can improve PSR effect. PSR enhances the weak high-frequency fault characteristic signal. Some simulations are designed for analysing MNTPSR-1/f β performance such as the MNT effect, anti-noise capability, and optimal frequency response. A fault diagnosis strategy for the planetary gearbox is proposed. To validate the proposed method, two pre-planting fault tests of sun gear and ring gear are carried out. For comparison, PSR without MNT, MNTPSR-1/f and MNTPSR-1/f β are used to analyse the fault signals. The simulations and case validations prove that our proposed method is the most effective. Abbreviations: wavelet packet transformation (WPT), stochastic resonance (SR) [ABSTRACT FROM AUTHOR]

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The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings' interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions. [ABSTRACT FROM AUTHOR]

Goal of this research was to develop and manufacture planetary gearbox prototype using rapid prototyping technology (additive manufacturing). Developed prototype was used to visually analyse the design of the planetary gearbox. Also, it was used to improve and innovate education of students on several courses at Mechanical Design study program at Faculty of Mechanical Engineering. It is shown that low cost rapid prototyping technology can be used to manufacture prototypes of complex machines and machine elements. Prototypes manufactured using this technology have same functionality like the real one. Main limitation is the fact that they cannot sustain real world loads and stresses. This paper shows opportunities which low cost rapid prototyping technology is offering in improvement and innovation of education process at engineering schools and faculties. All complex and heavy machines can be manufactured using this type of technology and on that way more precisely presented to the students. [ABSTRACT FROM AUTHOR]

Internal ring gears have a wide application in planetary gearboxes and transmission systems. The mesh stiffness of planetary gearboxes is an essential parameter for dynamic and vibration analysis. No direct practical method for calculating the stiffness of the internal ring gear rim has been investigated yet to the authors' knowledge. For the first time in this research, a practical method is presented to determine the stiffness of the internal ring gear rim with an appropriate accuracy based on strain energy. To accomplish this goal, every part of the internal ring gear rim, which is between two consecutive pins or bolts, is modeled as a curved beam. The deflection of the curved beam is determined according to Castigliano's theorem. Then, the stiffness of the internal ring gear rim is obtained through this deflection. Fourier's series of deflections are calculated to make analytical results more comfortable to use. Finally, the analytical results are validated by FEM using ABAQUS software. [ABSTRACT FROM AUTHOR]

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised. [ABSTRACT FROM AUTHOR]

Despite the progress made in the last decades in the field of machine condition monitoring, there are still cases where the current state of the art is not enough and new technologies and advanced analysis methods are required to prevent unexpected failures. One example is planetary gearboxes (PGs), which are one of the main components of mechanical transmission systems in heavy-duty machines such as off-highway trucks, electric rope shovels, helicopters and wind turbines. Although those machines are usually equipped with vibration and temperature sensors to detect faults in mechanical components, these technologies might not be able to perform well under certain circumstances. Therefore, the applied investigation on new monitoring technologies in the field of machine condition monitoring is a necessary step. Among those, in this review the acoustic emission technology will be addressed as a tool for fault diagnosis of gear faults in PG. [ABSTRACT FROM AUTHOR]

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

Planetary gearboxes are the key components of various rotating machineries, so it is of great significance to analyze their dynamic characteristics under the local gear faults for assuring the high reliability and service performance of equipment. Pitting is the most typical gear fault, and it can be approximately treated as part of a sphere. When multiple pittings are randomly distributed, they probably overlap, the effect of overlapping pittings on mesh stiffness is more complex. Additionally, sliding friction is another important factor affecting mesh stiffness. It follows that a new time-varying mesh stiffness model considering time-varying friction is established to characterize the spherical overlapping pittings. In particular, considering that the cross-section shapes of overlapping pittings are irregular, a piecewise integration method is proposed for calculating multiple tooth meshing contact lines. Then a multi-degree-of-freedom nonlinear fault dynamics model of planetary gearbox is built based on time-varying meshing force, time-varying friction force, and inertial force. Via different degrees of pittings at different locations, the simulation results validate the positive roles of the proposed time-varying mesh stiffness model and time-varying friction excitation in the dynamic responses of planetary gearboxes. Finally, the experiments on faulty planetary gearboxes are performed, and the comparative results verify the superiority of the proposed model over the existing models. [ABSTRACT FROM AUTHOR]

Accurate modeling of the vibration signal model of planetary gearboxes is essential for the subsequent fault diagnosis. According to the existing improved phenomenological model based on the meshing vibration, this paper conducts further investigations on the vibration mechanism of the gearbox. The time delay phenomenon of faulty ring gear tooth participating in meshing is theoretically analyzed, and the assisted phases for experimental verification are proposed and deduced. Based on the improved model, the vibration signal under the fault condition is simulated and compared with the results of traditional methods. Subsequently, the paper proposes to divide and reconstruct this signal and use the maximum correlation kurtosis deconvolution (MCKD) to enhance its impact characteristics. The results show that the phase between the fault impact and its adjacent meshing impact is consistent with the proposed assisted phase. Finally, the correctness of the vibration mechanism and the improved phenomenological model are verified experimentally. [ABSTRACT FROM AUTHOR]

Stator current modeling for defective planetary gearboxes based on magnetic equivalent circuits (MEC) is conducted in this article. A lumped parameter torsional model of the motor-planetary gearbox coupling system is established to obtain the nonlinear time-variable torsional response of the rotor numerically. An MEC model of an induction motor is established by connecting the equivalent flux tubes with nodes. After considering the time-variable rotor torsional response, the nonlinear magnetic field intensity of the iron material, and magnetic saturation, an iterative numerical integration method is presented to solve the MEC model. Both a finite element analysis and dynamic tests on a planetary gearbox driven by an induction motor are carried out for verification. Based on these, the fault characteristic components in the stator current spectra are, respectively, identified when the tooth chipping defect appears at sun/planet/ring gears. Fault-related frequencies are essentially represented by the combinations between the meshing frequency, sun/planet/ring gear fault passing frequency, its harmonics, and the power supply frequency. Owing to the electromechanical coupling effect, high-order harmonics of the power supply frequency also participate in the fault-related frequencies of the planet and ring gears. By quantitatively analyzing the influence of chipping size on the amplitudes of fault characteristic frequencies, suitable spectra for planetary gearbox condition monitoring are recommended. [ABSTRACT FROM AUTHOR]

This paper presents a novel signal processing scheme by combining refined composite hierarchical fuzzy entropy (RCHFE) and random forest (RF) for fault diagnosis of planetary gearboxes. In this scheme, we propose a refined composite hierarchical analysis based method to improve the feature extraction performance of existing MFE and HFE methods. First, RCHFE is applied to extract the fault-induced information from the vibration signals. Because a refined composite analysis is used in HFF, the feature extraction capability of HFF can be effectively enhanced. Then, the extracted features are fed into the RF for effective fault pattern identification. The superiority of the proposed RCHFE–RF method is validated using both simulated and experimental signals. Results show that the proposed method outperforms MFE-RF and HFE-RF in identifying fault types of planetary gearboxes. • RCHFE improves the feature extraction ability of FE and HFE. • RCHFE reduces the probability of undefined FE. • Simulated and experimental signals validate the advantage of our proposed RCHFE comparing with six existing entropy methods. [ABSTRACT FROM AUTHOR]

Structural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox. [ABSTRACT FROM AUTHOR]

The planetary transmission system has been widely used in industry because of its various advantages. And the study on degradation feature extraction method of planetary gearbox is of major significance for mechanical system prognostics and health management (PHM). In this paper, the alpha stable distribution characteristics of planetary gearbox vibration signals in performance degradation process are verified. By observing the change of alpha stable distribution for planetary gearbox degradation experiment data, a new degradation feature extraction method based on alpha stable distribution is proposed, which is called the height of probability distribution (HPD). Through comparative analysis, it is determined that HPD has better linearity and less fluctuation compared with conventional degradation features in planetary gearbox accelerated degradation stage. Moreover, in the accelerated degradation stage, the degradation trend prediction result based on HPD is closer to the actual data than conventional degradation features no matter using Wiener-based or LSSVM-based prediction method. These conclusions indicate that the newly proposed HPD works well and gives accurate estimates for condition monitoring and degradation trend prediction of planetary gearbox. [ABSTRACT FROM AUTHOR]

A resonance-based sparse improved fast independent component analysis (ICA) (RSIFICA) is proposed to extract the fault characteristics of planetary gearbox. First, the signal is decomposed using resonance sparse signal decomposition (RSSD). Second, high-resonance components were retained while others were eliminated. Finally, the signal after dimension reduction was analyzed using ICA, and the fault characteristic frequency was extracted through envelope spectrum analysis. In this process, the preset Q parameter of RSSD is optimized on the basis of fuzzy entropy and ant-lion optimization algorithm. The accuracy of RSSD was improved by performing time-frequency entropy component selection. FastICA was improved, and the slow convergence problem of ICA was solved. Results showed that RSIFICA could extract the fault characteristic frequency accurately, and the calculation efficiency of FastICA increased by 21.49 %. In terms of extracting the fault features, its performance could be better than EMD-FastICA. [ABSTRACT FROM AUTHOR]

• A multitasking Y-Net that incorporates SE-Res modules in convolutional network is proposed. • A soft joint maximum mean discrepancy is proposed to extract domain-invariant features. • The domain adaptive fault diagnosis of different planetary gearbox under various operating conditions is realized. • The target-equipment dataset has no label, and the proportion of various categories is different from the source-equipment. • Y-Net remarkably outperforms related methods on all domain adaptation task. The planetary gearbox plays an important role in many advanced electromechanical mechanisms. The mechanical fault is a major factor that threatens the service performance of a planetary gearbox. Deep learning (DL) algorithms have been widely used to identify faults and health status of industrial equipment. However, owing to diversified equipment structures, variable working conditions and disparate data acquisition, the service performance of DL-based methods may degrade significantly when applied to different industrial sites. Domain adaptation emerges as a promising idea that aims to transfer knowledge from a source domain to a different but related target domain. In this paper, we introduce a novel deep domain-adaptive multi-task learning model Y-Net, which is exploited to enable domain-adaptive diagnosis of faults in planetary gearboxes. The SE-Res modules are utilized to reduce the redundancy of the model and improving the separability of deep features. Furthermore, a soft joint maximum mean discrepancy (SJMMD) is introduced to link the two pipeline in order to reduce both the marginal and conditional distribution discrepancy of the learned features, with the enhancement of auxiliary soft labels. The domain adaption between different planetary gearbox under variant operating condition is realized by the Y-Net. Experiments demonstrate the superiority of the proposed SJMMD over conventional maximum mean discrepancy, especially when the datasets of different domains suffer different imbalances. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of the impact of oil viscosity on the vibroacoustic signal generated by a planetary gearbox operating at variable load. A diagnostic experiment was conducted at the laboratory rig for testing planetary gearbox which was lubricated with a dedicated ISO 150 viscosity class oil and much lower viscosity class 5W40 oil. The gearbox was subjected to two types of sinusoidal load and to a random load corresponding to the load of the main gearbox of the KWK 1500s bucket wheel excavator used in opencast mining. The diagnostic method was proposed based on the analysis of the dependence of the gearbox characteristic orders amplitudes on the load. The effectiveness of the method in detecting the changes in vibration acceleration signals caused by the oil viscosity changes was proved. [ABSTRACT FROM AUTHOR]

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy. [ABSTRACT FROM AUTHOR]

Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection. Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis. • EAVG filter is used to extract the positive and negative impulses. • Kurtosis is taken as a novel criterion to optimize the SE length of EAVG. • MSB is used to extract fault feature for planetary gearbox fault detection. • Experimental results prove that the EAVG-MSB outperforms the ACDIF and CMFH. [ABSTRACT FROM AUTHOR]

In industrial field, there is an increasing demand for monitoring systems enabling predictive maintenance programs. In this context, the present work concerns the monitoring of distributed wear (pitting) in planetary gearboxes. For this purpose, some metrics of the synchronous average of the vibration signal, based on the statistical moment of the fourth order, are present in literature; in this paper, a new indicator is proposed, namely NA4mod. The effectiveness of this metric in identifying the early stage of pitting has been evaluated by conducting an accelerated life test of about 700 hours on a test bench using a back-to-back configuration. The paper introduces the proposed metric, describes the test, presents and dis-cusses the results. Metric NA4mod exhibits satisfactory capability to detect pitting with better reliability than other metrics in literature. In addition, the metric is shown to be sensitive to both early stage damage and pitting severity in the final stage. Results are verified by means of wavelet-transform analysis. [ABSTRACT FROM AUTHOR]

At present time three main widespread methods of planetary gearboxes analysis are used - analytical calculation, practical tests and simulations. This paper deals with verification of the Simscape driveline module of the Matlab software as a tool of vehicle transmission and driveline designing. Different parameters results of analytical computing and Simscape simulations output values are compared in this paper. Deviations of these values turned out to be insignificant and therefore Simscape module proved to be applicable, usable, cost and time reducing and very useful in transmissions designing process. [ABSTRACT FROM AUTHOR]

Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox. [ABSTRACT FROM AUTHOR]