Gearbox Diagnosis Research Articles

A temperature rise calculation model of wind turbine gearbox gear considering crack fault and tooth number difference

AbstractThe crack fault of gear is usually accompanied by temperature rise. Therefore, to master the temperature characteristics of the cracked gear of the wind turbine gearbox, a temperature rise calculation model of wind turbine gearbox gear considering crack fault and tooth number difference is proposed. Firstly, the time‐varying meshing stiffness model of the cracked gear considering the tooth number difference is established based on the potential energy method. Secondly, the calculation method of the meshing surface normal load of the cracked gear is deduced. Thirdly, the temperature rise calculation model of the meshing surface of the cracked gear is constructed based on Blok flash temperature theory. Finally, the data of the high‐speed gear of a wind turbine gearbox in northern China is selected for simulation verification. By comparing with the finite element method, the effectiveness of the proposed method is verified. The simulation results reveal the gear temperature characteristics of the wind turbine gearbox with different crack ratios. The research can provide some theoretical support for the accurate fault diagnosis and maintenance of wind turbine gearbox, and can also be applied to the fault diagnosis of gear cracks in other mechanical structures with a large transmission ratio.

Fault diagnosis of planetary gearboxes under small and imbalanced samples based on enhanced Siamese network with improved downsampling module

Due to the limitations of the operating conditions and equipment status, in practice, the normal data for planetary gearboxes are often sufficient, but the fault data are scarce and difficult to obtain, causing the fault diagnosis to face not only an imbalance problem but also a small sample problem. It leads to serious misdiagnosis and poor generalizability. To overcome above challenges, an enhanced Siamese network with improved downsampling module is proposed to reduce the negative impact of small and imbalanced datasets on fault diagnosis. Firstly, the Siamese network is combined with data enhancement to adaptively increase the fault training data with different small sample degrees. It can make the number of training data of each class get rid of small sample, while avoiding the excessive use of scarce fault data causing additional overfitting, so as to improve the generalization of the model. Among them, the data enhancement adopts the extreme noise expansion method proposed in this work, which can quickly generate samples that meet the requirements and help to improve the performance of the model in noisy environments. Secondly, adding the improved downsampling module to the Siamese network. The improved downsampling module introduces density-based spatial clustering of applications with noise (DBSCAN) and distribution ratio calculation, which can consider the distribution range and density of data when selecting. It can not only balance the training data, but also avoid serious information loss, and then reduce the probability of misdiagnosis. Finally, using six imbalanced planetary gearbox datasets with different small sample degrees for verification. The experimental results show that the proposed method can efficiently increase and balance the training data, which greatly improves the accuracy of fault diagnosis under the condition of small and imbalanced samples, especially when the small sample degree is very serious, the accuracy is improved by 57.76% on average.

Small sample gearbox fault diagnosis based on improved deep forest in noisy environments

ABSTRACT Deep forest methods have gradually emerged as a well-liked substitute for conventional deep neural networks in diagnosing faults in mechanical systems. However, in practical industrial applications, limited training data and severe noise interference pose significant challenges to these models. Existing deep forest models have limitations in information extraction, making it difficult to handle the complexities of industrial environments. To better meet the practical needs of industrial applications, this paper proposes an improved deep forest model – sgic-Forest, specifically designed for fault diagnosis of small sample gearboxes in noisy environments. First, we developed a stacked multi-grained scanning module, which enhances the diversity of feature extraction by integrating the advantages of multiple base learners, thereby better addressing the complexities of industrial data. Secondly, we introduced an important feature selection module, which effectively filters out irrelevant information, significantly improving the model’s robustness in high-noise environments. Experiments on two gearbox datasets show that the proposed method outperforms the basic deep forest model and mainstream deep learning methods in terms of diagnostic accuracy under small sample and noisy conditions.

A gear fault diagnosis method based on reactive power and semi-supervised learning

Abstract In gearbox gear fault diagnosis based on motor current signals, the gear fault characteristic frequency component is often overshadowed by the fundamental frequency component of the current. In addition, the complex working conditions during actual production and use make it difficult to collect gear operation monitoring data containing labeled feature information. To address the above problems, a semi-supervised learning method based on reactive power signals is proposed for gear fault diagnosis of gearboxes. First, the method utilizes the Hilbert transform to process the current signal of the drive motor in the mechanical system, from which the reactive power is constructed. Then, the reactive power signal is analyzed by spectral analysis as a basis for gear fault diagnosis. Subsequently, the GAF-CNN-MTDL(Gramian angular field—convolutional neural network-mean teacher deep learning) fault diagnosis model is proposed to convert the reactive power signal into a two-dimensional image by using the GAF, and the semi-supervised training method of the average teacher is applied to input the fault dataset into the gear fault diagnosis model which is based on the CNN as the main backbone after the fault dataset has been divided into the labeled and the unlabeled dataset in accordance with a certain ratio. Finally, the gear fault dataset is used for method validation. The experimental outcomes demonstrate the method’s proficiency in effectively emphasizing the fault feature information pertaining to the gear part, and the introduced GAF-CNN-MTDL fault diagnosis model enables the utilization of a minimal number of labeled samples to achieve highly accurate gear fault diagnosis.

Gearbox fault diagnosis based on RGT-MFFIN and multi-sensor fusion image generation

Abstract In gearbox fault diagnosis based on vibration and torque state data, traditional one-dimensional time-frequency domain analysis methods often suffer from insufficient feature expression and mining, and require complex noise reduction and filtering preprocessing. To address this issue, this paper proposes a fusion image generation method that integrates the advantages of recurrence plot (RP) and Gramian angular summation field (GASF) to generate recurrence Gramian transformed (RGT) images. This approach integrates both global and local fault information, making the fault characteristics more intuitive and easier to analyze. Given that multi-sensor collaboration can enhance feature representation, feature-level fusion increases the computational burden, and decision-level fusion is prone to losing inter-sensor correlation information, this paper adopts data-level fusion for image sample enhancement. In the diagnostic method, the challenge of traditional convolutional neural networks (CNNs) in extracting diverse geometric linear structures from fused images is addressed by introducing deformable convolutional blocks for initial feature extraction. Additionally, a multi-scale feature fusion interaction network (MFFIN) is constructed. This network incorporates a channel-space interactive attention mechanism on top of multi-scale feature extraction, assigning weights to features according to their importance while facilitating the interaction of feature information. Finally, validation is carried out using public datasets, and the experimental results show that the proposed method demonstrates significant advantages in classification accuracy and robustness under variable operating conditions and noise, thereby proving its effectiveness and practicality.

Composite fault diagnosis of gearbox based on deep graph residual convolutional network

In gearbox systems, a composite fault diagnosis resulting from mutual interference among different components poses a significant challenge. The traditional composite fault diagnosis methods based on conventional signal analyses and feature extractions often suffer from low sensitivity to fault characteristics and difficulty in effectively identifying composite faults. On the other hand, composite fault diagnosis research via deep learning and data-driven approaches typically faces issues such as incomplete training datasets and insufficient exploration of feature correlation information, leading to an underutilization of the fault information. Therefore, this paper proposes a deep graph residual convolutional neural network (DGRCN) based on feature correlation mining for composite fault diagnosis in gearboxes. First, Pearson correlation coefficients are utilized to explore the relationships among features in the traditional feature set, transforming these relationships into a graph-structured feature set. Next, a deep graph residual convolutional network is constructed by integrating deep graph structures into a residual framework. This network globally extracts composite fault subgraph features and explores local feature correlations. Finally, the model is trained via various composite fault datasets under complex working conditions, achieving the diagnosis and identification of composite faults under the constraint of limited samples. The experimental results demonstrate that the proposed method significantly improves composite fault diagnosis accuracy, outperforming commonly used methods in this field.

Fault diagnosis of planetary gearboxes under variable operating conditions based on AWM-TCN

In the fault diagnosis of planetary gearboxes under varying operating conditions, fault features are often submerged and difficult to characterize. To address this issue, this paper proposes a fault diagnosis method for planetary gearboxes based on an Attention Mechanism Wide-scale Multi-channel Temporal Convolutional Network (AWM-TCN). This method introduces an attention mechanism into the TCN framework, ensuring the model learns the spatiotemporal characteristics of fault signals while enhancing the importance of fault-related features. The experimental section innovatively employs three different validation methods for multi-angle evaluation of the model, discusses the impact of different structures on the proposed model, and compares it with various classical models. The results indicate that AWM-TCN can achieve effective diagnosis of planetary gearboxes under varying conditions and demonstrates rapid responsiveness.

Gearbox Fault Diagnosis Based on MSCNN-LSTM-CBAM-SE.

Ensuring the safety of mechanical equipment, gearbox fault diagnosis is crucial for the stable operation of the whole system. However, existing diagnostic methods still have limitations, such as the analysis of single-scale features and insufficient recognition of global temporal dependencies. To address these issues, this article proposes a new method for gearbox fault diagnosis based on MSCNN-LSTM-CBAM-SE. The output of the CBAM-SE module is deeply integrated with the multi-scale features from MSCNN and the temporal features from LSTM, constructing a comprehensive feature representation that provides richer and more precise information for fault diagnosis. The effectiveness of this method has been validated with two sets of gearbox datasets and through ablation studies on this model. Experimental results show that the proposed model achieves excellent performance in terms of accuracy and F1 score, among other metrics. Finally, a comparison with other relevant fault diagnosis methods further verifies the advantages of the proposed model. This research offers a new solution for accurate fault diagnosis of gearboxes.

Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm

The complex structure and harsh operating environment of wave energy converters can result in various faults in transmission components. Environmental noise in practical operating situations may obscure the effective information in collected vibration signals, significantly increasing the difficulty of fault diagnosis. This paper presents a fault diagnosis model for the gearbox of the point absorber wave energy converter. The model integrates a convolutional neural network with long short-term memory to realize efficient extraction of local features from signals and enhance the performance in time-series analysis. Moreover, the model incorporates the Adaptive Moment Estimation algorithm to address the situations where gradients within tensors exhibit unstable changes in the model. A rigid-flexible coupled dynamics simulation model is developed to simulate vibration signals used to train and verify the fault diagnosis model. Experimental tests of the proposed model on a vibration dataset acquired from real vibration experiments demonstrate its efficacy in diagnosing various types of faults under interference of operating conditions. Comparative studies with other models demonstrate the superiority of the proposed model in terms of fault feature extraction, learning convergence efficiency, and diagnostic accuracy, indicating that the proposed model can achieve faster and more accurate fault diagnosis of wave energy converter gearboxes.

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Abstract Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

Quantitative Fault Diagnosis of Planetary Gearboxes Based on Improved Symbolic Dynamic Entropy

To realize a quantitative diagnosis of faults in the planetary gearboxes of wind turbines by processing the complex frequency signals of the planetary gear boxes and avoiding the aliasing problem of the resulting frequencies, this paper proposes a diagnosis method based on improved variational mode decomposition (IVMD) and average multi-scale double symbolic dynamic entropy (AMDSDE). Moreover, an IVMD algorithm based on multi-scale permutation entropy is introduced to reduce noise interference and realize signal demodulation. Considering the effects of complex transfer paths and the correlation between current and adjacent state modes, AMDSDE is proposed. Each fault size is obtained based on the entropy curve, and the AMDSDE of unknown faults is calculated. To verify the accuracy of the proposed method, simulations and experimental signals are processed. The quantitative diagnosis of the planetary gearboxes of wind turbines is realized, providing a reliable basis for evaluating the health status of planetary gearboxes.

A distinguished deep learning method for gear fault classification using time–frequency representation

Fault diagnosis of gearboxes has attracted increasing interest in recent decades due to their ubiquity and importance in the industry. Modern research trends focus on developing a diagnosis system that works automatically with the application of artificial intelligence. These previous studies have used the Deep Learning (DL) network without adequately addressing noise of the input data, requiring more data to achieve effective training. Thus, this work proposes a novel Transfer Learning method using the time–frequency representation of gear vibration signals, which enables more accurate classification in complex working conditions and reduces necessary input data to train. Using fine-tuning techniques proposed in this paper requires only a limited data set while ensuring acceptable classification results. An experiment test rig within different gear faults and load conditions was set up to evaluate the algorithm’s effectiveness.

Exploring evolutionary-tuned autoencoder-based architectures for fault diagnosis in a wind turbine gearbox

ABSTRACT Vibration-based fault diagnosis from rotary machinery requires prior feature extraction, feature selection, or dimensionality reduction. Feature extraction is tedious, and computationally expensive. Feature selection presents unique challenges intrinsic to the method adopted. Nonlinear dimensionality reduction may be achieved through kernel transformations, however there is often a trade-off in information to achieve this. Given the above, this study proposes a novel autoencoder (AE) pre-processing framework for vibration-based fault diagnosis in wind turbine (WT) gearboxes. In this study, AEs are used to learn the features of WT gearbox vibration data while simultaneously compressing the data, obviating the need for costly feature engineering and dimensionality reduction. The effectiveness of the proposed framework was evaluated by training genetically optimized linear discriminant analysis (LDA), multilayer perceptron (MLP), and random forest (RF) models, with the AE’s latent space features. The models were evaluated using known classification metrics. The results showed that the performance of the models depends on the size of the AE’s latent space. As the size of the AE’s latent space increased, the quality of features extracted improved until a plateau was observed at a latent space dimension of 10. The AE pre-processed genetically optimized RF, MLP, and LDA models, designated AE-Pre-GO-RF, AE-Pre-GO-MLP, and AE-Pre-GO-LDA, were evaluated for accuracy, sensitivity, and specificity in the classification of seven (7) gearbox fault conditions. The AE-Pre-GO-RF model outperformed its counterparts, scoring 100% for all evaluated metrics, though with the longest training time (239.50 sec). Comparable results were found comparing this study with similar investigations involving traditional vibration processing techniques. More so, it was established that effective fault diagnosis of the WT gearbox can be achieved through manifold learning with AEs without expensive feature engineering.

Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping.

Vibration monitoring and analysis are important methods in wind turbine gearbox fault diagnosis, and determining how to extract fault characteristics from the vibration signal is of primary importance. This paper presents a fault diagnosis approach based on modified hierarchical fluctuation dispersion entropy of tan-sigmoid mapping (MHFDE_TANSIG) and northern goshawk optimization-support vector machine (NGO-SVM) for wind turbine gearboxes. The tan-sigmoid (TANSIG) mapping function replaces the normal cumulative distribution function (NCDF) of the hierarchical fluctuation dispersion entropy (HFDE) method. Additionally, the hierarchical decomposition of the HFDE method is improved, resulting in the proposed MHFDE_TANSIG method. The vibration signals of wind turbine gearboxes are analyzed using the MHFDE_TANSIG method to extract fault features. The constructed fault feature set is used to intelligently recognize and classify the fault type of the gearboxes with the NGO-SVM classifier. The fault diagnosis methods based on MHFDE_TANSIG and NGO-SVM are applied to the experimental data analysis of gearboxes with different operating conditions. The results show that the fault diagnosis model proposed in this paper has the best performance with an average accuracy rate of 97.25%.

A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition

Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor–shaft–disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.

Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network

This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology.

Fault Diagnosis of Gearbox Based on CNN and LSTM with Attention Mechanism

Gearbox is a key component of mechanical equipment, which has a complex structure, harsh working conditions, and a higher probability of failure. Therefore, gearbox fault diagnosis is vital to ensure the efficient operation of the whole mechanical equipment. However, traditional gearbox fault diagnosis methods mainly rely on manual feature extraction. To address this issue, a novel end-to-end fault diagnosis model that combines convolutional neural network (CNN), long short-term memory network (LSTM) and attention mechanism (AM) is proposed. Firstly, Spatial features are extracted from the original input data using CNNs, and then temporal features are extracted even further from the spatial features using LSTMs. The attention mechanism improves the network's attention to the global key features by assigning weights, and finally, the fault diagnosis results are derived from the fully connected layer and Softmax classifier. The experimental results show that the method is able to adaptively extract features and ensure higher diagnostic accuracy, validating the effectiveness of the proposed method.

Study on the Vibration Characteristics of the Helical Gear-Rotor-Bearing Coupling System of a Wind Turbine with Composite Faults

As the core component of the wind turbine generation gearbox, the gear-rotor-bearing transmission system typically operates in harsh environments, inevitably leading to the occurrence of composite faults in the system, which exacerbates system vibration. Therefore, it is necessary to study the vibration characteristics of wind turbine helical gear-rotor-bearing transmission systems with composite faults. This paper uses an improved energy method to calculate the theoretical time-varying mesh stiffness of a helical gear with a root crack failure. On the premise of considering the time-varying meshing stiffness of the faulty helical gear, the gear eccentric fault, and the nonlinear support force of the faulty bearing, a multi-degree-of-freedom helical gear-rotor-bearing transmission system with compound faults was established by using the lumped parameter method. The dynamic model of the system was solved based on the Runge–Kutta method, and the vibration response of the system under healthy conditions, single faults with gear eccentricity, single faults with tooth root cracks, and coupled bearing composite faults were simulated and analyzed. The results show that the simulation results based on KISSsoft software 2018 version verify the effectiveness of the improved energy method; the existence of single faults and composite faults will cause the fault characteristics in the time domain and frequency domain responses. In this paper, the influence of a single fault and a complex fault on the time domain and frequency domain of the system is mainly discovered through the fault study of the helical rotor-bearing system, and the influence of the fault degree on the vibration of the gear motion system is discussed. The greater the degree of the fault, the more vibration of the system occurs; accordingly, when the system is under the coupling of tooth root crack and bearing fault, there is a significant difference compared with the healthy system and the single fault system. The system vibration has obvious time domain and frequency domain signal characteristics, including periodic pulse impacts caused by gear faults and time domain impact caused by bearing. The fault characteristic frequencies can also be found in the frequency domain. In this paper, the fault study of a helical gear of wind turbine generation provides a reference for the theoretical analysis of the vibration characteristics of the helical gear-rotor-bearing system under various fault conditions, lays a solid foundation for the simulation and subsequent diagnosis of the composite fault signal of the system, and provides help for the fault diagnosis of wind turbine gearboxes in the future.

Detect Gear Fault in Wind Turbine Gearbox based on Time Synchornous Averaging Without Phase Signal.

Wind energy is increasingly recognized as a worldwide sustainable and environmentally friendly power source. Nevertheless, an important barrier to further investment in wind energy is the large rate of failure that occurs to turbines. The gearbox, a crucial component, has a major effect on the performance of wind turbines. It consists of complex planetary and cylindrical gear systems, making it prone to failure and causing major defects in wind turbines. Therefore, there is an urgent need to minimize downtime and improve productivity in wind turbine gearbox operations. Recent decades have witnessed growing interest in fault diagnosis of gearboxes due to their widespread use and industry significance. The time synchronous averaging (TSA) method is widely used as a fundamental approach to detect faults in wind turbine gearboxes from mechanical vibration signals. Usually, applying this method requires a device to measure the vibration phase. Nevertheless, there are certain circumstances where installing a phase-measuring device might pose challenges. For instance, if the gearbox operates, it cannot be paused for installation. Additionally, if the gearboxes are enclosed, it becomes difficult to insert the device. The present paper presents an innovative technical approach to improve the time synchornous averaging method without requiring information about phase vibration. It also evaluates the effectiveness of the proposed method using feature values. An experimental test rig was set up to evaluate the algorithm's effectiveness, simulating different gear faults and load conditions.

Investigation on the fusion reliability and cluster consistency of multivariable entropy method

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