Planetary Gear Fault Research Articles

Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN

To solve the problem that the existing planetary gear train fault diagnosis methods have, namely their low diagnostic accuracy under low signal-to-noise ratio (SNR), a fault diagnosis method based on a double channel-deep residual shrinkage network (DC-DRSN) is proposed. The short-time Fourier transform (STFT) is used to convert the original vibration signal into a two-dimensional time-frequency graph, which effectively enhances the ability to express information. A DC-DRSN model is constructed, and the optimal number of residual shrinkage modules is determined by combining the diagnostic characteristics with different noises, which effectively improves the accuracy and anti-noise ability of fault diagnosis. The results of bearing and planetary gear train crack fault diagnosis show that the diagnosis method based on DC-DRSN has higher diagnostic accuracy while realizing fault diagnosis, which is better than other deep learning diagnosis methods. At the same time, the method can adapt to fault diagnosis in different noise environments, and has good expression ability and generalization ability.

Wavelet-driven differentiable architecture search for planetary gear fault diagnosis

With the advancement of artificial intelligence and the accumulation of industrial big data, intelligent diagnosis methods based on deep learning have become the mainstream for diagnosing mechanical faults in manufacturing systems. Despite this, developing high-performance neural network models for specific tasks necessitates a substantial amount of expert knowledge. This consumes a considerable amount of time in the trial-and-error process, thereby constraining the progress in neural network development. Neural architecture search (NAS) offers a solution to address this problem. However, the feature extraction operators in the existing NAS search space are primarily imported from other fields, leading to domain bias and a lack of interpretability. To address these challenges, we present a NAS method driven by wavelets. To be specific, the wavelet operators that can extract fault related features from vibration signals is added to the search space, and the gradient optimization strategy is utilized to search the optimal architecture from the hypernet. The effectiveness of the proposed method is validated through a dataset specific to planetary gears. Upon comparison with other models, it is evident that the proposed method exhibits superior performance across all added noise levels.

Interpretable Siamese dual attention enhancement transfer compound diagnostic model for unbalanced samples

The intelligent transfer diagnosis model is used to address the issue of feature drift caused by the changing working conditions of rotating parts in engineering. However, few models can perform transfer diagnosis on multiple unbalanced samples of rotating parts simultaneously, and even fewer models can visually enhance the domain-invariant features, making them more interpretable. To address these issues, we propose a novel interpretable Siamese dual attention enhancement transfer compound diagnosis model for unbalanced samples. The model can diagnose multiple rotating parts simultaneously and consists of a channel feature attention enhancement (CFAE) network, a fragment feature attention enhancement (FFAE) network, and a Siamese feature fusion (SFF) network. The CFAE network enhances features of different convolutional channels, the FFAE network improves segment features in various frequency domains, and the SFF network extracts domain-invariant features of diverse rotating components under varying working conditions. The model is validated using bearing fault data collected under different loads and planetary gear fault data obtained at varying speeds. Its diagnostic accuracy remains above 96.4%, and the diagnostic variance is controlled within 1.0%. The model has good interpretability for imbalanced sample domain-invariant features, providing an effective tool for interpretable transfer diagnosis in this compound engineering situation.

Gearbox fault diagnosis method based on improved semi-supervised MTDL and GAF

Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.

Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin

Aiming at the problems of the traditional planetary gear fault diagnosis method of wind turbines, such as the poor timeliness of data transmission, weak visualization effect of state monitoring, and untimely feedback of fault information, this paper proposes a planetary gear fault diagnosis method for wind turbines based on a digital twin. The method was used to build the digital twin model of wind turbines and analyze the wind turbines’ operating state utilizing virtual and real data. Empirical mode decomposition (EMD) was used, and an atom search optimization–support vector machine (ASO-SVM) model was established for planetary gear fault diagnosis. The digital twin model diagnoses faults and constantly revises the model based on the diagnostic results. The digital twin fault diagnosis system was implemented in the Unity3D platform. The experimental results demonstrate the feasibility of the proposed early-warning system for the real-time diagnosis of planetary gear faults in wind turbines.

Effects of crack and structural flexibility on planetary gear system fault feature considering ring gear boundary condition

The crack will inevitably aggravate the transmission system vibration. In addition, the structure of planetary gear train is much more complex, and due to the installation conditions and other factors, the boundary conditions of ring gear are also different (including the pin support constraint and outer rim constraint). In view of the deficiencies of the existing research on the planetary gear train crack morphology, which are mostly based on simple assumptions and lack of experimental verification, the crack growth test is carried out and the theoretical crack morphology is verified. Then, the vibration feature is analyzed based on the obtained crack morphology and the established system-level dynamic model of the planetary gear train (composed of housing, carrier, gear and shaft, etc.). The studies reveal that the fault feature is more obvious when the initial crack location is far away from the tooth root. Under the boundary condition of outer rim constraint, the thinner ring gear can decrease the dynamic response and crack impact, but when restraining the pin support, the observed phenomenon is opposite. After considering the housing flexibility, the vibration is reduced, however, the increasing flexibility weakens the crack fault feature of the planetary gear train.

Enhanced weight symplectic geometry decomposition based on maximum periodic kurtosis deconvolution

To enhance the periodic impact component and improve the accuracy of planetary gear fault detection, an enhanced weighted symplectic geometry decomposition based on maximum periodic kurtosis deconvolution (MPKD-EWSGD) is proposed in the paper. On the one hand, MPKD-EWSGD adopts the MPKD method for noise reduction preprocessing to highlight the periodic impulse component. On the other hand, MPKD-EWSGD introduces the periodic impulse intensity (PII) to choose components with fault information, avoiding the disadvantages of denoising methods that use the component energy as the measurement standard. Emulation and experimental signals show that MPKD-EWSGD can effectively reduce noise and is an effective method for planetary gearbox fault detection.

Multi-Body Dynamics Modeling and Analysis of Planetary Gearbox Combination Failure Based on Digital Twin

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.

A New Resultant Vibration Acceleration Model of a Planetary Gear Train and Fault Response Analysis

A new resultant vibration acceleration model is proposed to reveal its signal characteristics more accurately in the healthy and faulty state. First, an analytical lateral-torsional coupled dynamic model is developed with consideration of time-varying mesh stiffness and damping, static transmission error, and gear backlash. Then, the effect of gear backlash and damping on the system is analyzed, and a numerical velocity signal in the healthy and faulty state is carried out. Considering the effects of transmission paths, the resultant acceleration signal along the vertical direction is constructed as the weighted vibration summation. This signal includes a vertical component of the vibration along the meshing lines of both sun-planet and ring-planet pairs. Moreover, it also contains the vertical component of the planet gears, sun gear, and planet carrier acceleration relative to their own supporting bearings. Finally, the simulation results from the resultant signal model are experimentally validated and analyzed in both time and frequency domains.

Fault Diagnosis of Planetary Gear Based on FRWT and 2D-CNN

The fault signals of planetary gears are nonstationary and nonlinear signals. It is difficult to extract weak fault features under strong background noise. This paper adopts a new filtering method, fractional Wavelet transform (FRWT). Compared with the traditional fractional Fourier transform (FRFT), it can improve the effect of noise reduction. This paper adopts a planetary gear fault diagnosis method combining fractional wavelet transform (FRWT) and two-dimensional convolutional neural network (2D-CNN). Firstly, several intrinsic mode component functions (IMFs) are obtained from the original vibration signal by AFSA-VMD decomposition, and the two components with the largest correlation coefficient are selected for signal reconstruction. Then, the reconstructed signal is filtered in fractional wavelet domain. By analyzing the wavelet energy entropy of the filtered signal, a two-dimensional normalized energy characteristic matrix is constructed and the two-dimensional features are input into the two-dimensional convolution neural network model for training. The simulation results show that the training effect of this method is better than that of FRFT-2D-CNN. Through the verification of the test set, we can know that the fault diagnosis of planetary gears can be realized accurately based on FRWT and 2D-CNN.

Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.

Mahalanobis-ANOVA criterion for optimum feature subset selection in multi-class planetary gear fault diagnosis

The empirical analysis of a typical gear fault diagnosis of five different classes has been studied in this article. The analysis was used to develop novel feature selection criteria that provide an optimum feature subset over feature ranking genetic algorithms for improving the planetary gear fault classification accuracy. We have considered traditional approach in the fault diagnosis, where the raw vibration signal was divided into fixed-length epochs, and statistical time-domain features have been extracted from the segmented signal to represent the data in a compact discriminative form. Scale-invariant Mahalanobis distance–based feature selection using ANOVA statistic test was used as a feature selection criterion to find out the optimum feature subset. The Support Vector Machine Multi-Class machine learning algorithm was used as a classification technique to diagnose the gear faults. It has been observed that the highest gear fault classification accuracy of 99.89% (load case) was achieved by using the proposed Mahalanobis-ANOVA Criterion for optimum feature subset selection followed by Support Vector Machine Multi-Class algorithm. It is also noted that the developed feature selection criterion is a data-driven model which will contemplate all the nonlinearity in a signal. The fault diagnosis consistency of the proposed Support Vector Machine Multi-Class learning algorithm was ensured through 100 Monte Carlo runs, and the diagnostic ability of the classifier has been represented using confusion matrix and receiver operating characteristics.

Improved multiclass support vector data description for planetary gearbox fault diagnosis

Planetary gearbox is one of the most important components of rotating machinery and plays a key role in modern industry. Due to the complex physical structures and harsh working conditions, planetary gearbox often suffers from different fault types, so it is of vital importance to investigate its fault diagnosis task. In this paper, a novel feature selection strategy is proposed to improve the multiclass support vector data description (SVDD) algorithm for planetary gearbox fault diagnosis. First, a novel feature selection method based on the cosine similarity measure in kernel space of Gaussian radial basis function (GRBF) is presented, so as to determine features that are sensitive to faults. Then, based on the selected features, an improved multiclass SVDD algorithm is developed to classify multiple classes of planetary gear faults, thus completing the fault diagnosis task. Finally, the effectiveness and advantage of the proposed method are demonstrated via experiments using wind turbine drivetrain diagnostics simulator (WTDDS), with comparison to several traditional methods.

A New Health Condition Detection Method for Planetary Gears Based on Modified Distributed Compressed Sensing and Multiscale Symbol Dynamic Entropy

Planetary gear transmission system is an important transmission part of large machinery and is prone to failure. Aiming at the problem of how to extract fault information from vibration signals of nonlinear and nonstationary planetary gearboxes, a performance degradation evaluation index of planetary gearboxes based on improved distributed compressed sensing and modified multiscale symbolic dynamic entropy (DCSMDE) is proposed. DCSMDE combines distributed compression sensing with modified multiscale symbol dynamic entropy and solves the problem of strong nonlinearity and strong vibration signal coupling of the planetary transmission system from the homologous signals of multiple sensors. A distributed compression sensing parameter optimization algorithm based on Rényi entropy is proposed, which uses improved distributed compression sensing technology to simultaneously sample, compress, and denoise the multisource vibration data of rotating machinery. DCSMDE is used to calculate the reconstructed signal, extract the features with higher recognition characteristics, and use the change trend of the DCSMDE value to judge the working status of the planetary gearbox. Experimental results show that DCSMDE can be applied to dynamic evolution and fault identification of mechanical systems and accurately classify actual fault signals. It provides a new idea for the classification of planetary gear faults and the recognition of performance degradation.

Fast convolution sparse filtering and its application on gearbox fault diagnosis

Transmission, as a critical part of vehicles, is the hub of power transmission and the core of controlling speed change. Condition monitoring and diagnosis of transmissions have become an effective tool to ensure vehicle safety travelling. The intelligent fault diagnosis strategy using artificial intelligent methods has been studied and applied for gearbox fault diagnosis. However, most algorithms cannot guarantee both accuracy and training efficiency. In this paper, fast convolutional sparse filtering based on convolutional activation and feature normalization is proposed for gearbox fault diagnosis without any time-consuming preprocessing. In fast convolutional sparse filtering, the features of samples are optimized instead of local features, which could obviously reduce the dimension and construction time of the Hessian matrix. In addition, the output features are equally active to guarantee that all features have similar contributions. The l2-norm of the training features is recorded and used for pseudo-normalization of the test features. The proposed fast convolutional sparse filtering is validated by a bearing fault dataset and a planetary gear fault dataset. Verification results confirm that fast convolutional sparse filtering is a promising tool for fault diagnosis, which has obviously improved the diagnosis accuracy, training efficiency, and robustness and provides the greater advantage of handling large-scale datasets.

Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions.

Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions.

Planetary Gear Faults Detection in Wind Turbine Gearbox Based on a Ten Years Historical Data From Three Wind Farms

Gear faults contribute to a significant portion of failures in wind turbine system. As such, condition monitoring and fault detection of these components assist in maintenance scheduling; hence, preventing catastrophic failures of the gearbox. This paper introduces a new hybrid fault detection approach to detect gear faults in wind turbines. to accomplish this task, vibration signals are collected and used to extract various time-domain features. Next, a Dynamic Principle Component Analysis (DPCA) is adaptively employed to identify failure dynamics by reducing the time-domain feature dimension. Following that, a Support Vector Machine (SVM) is implemented to detect and isolate gear faults. Experimental test studies with ten-year historical data of three wind farms in Canada are conducted. Test results indicate that the proposed hybrid approach performs superior compared to DPCA using Multilayer Perceptron (MLP) Neural Networks (NNs).

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

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.