Fault Diagnosis Method Of Rolling Research Articles

An intelligent fault diagnosis method for rolling bearing using motor stator current signals

Abstract In the diagnosis of rolling bearing faults, the Motor Current Signature Analysis (MCSA) method offers advantages such as low cost, simplicity, and convenience compared to using vibration signals, temperature information, and other diagnostic objects. However, owing to the interference of high-frequency noise, power frequency, and its harmonics in current signals, which can severely affect the accuracy of bearing fault diagnosis, it is extremely challenging to use the original current signals during bearing faults directly for diagnostic purposes. Therefore, this paper proposes an intelligent fault diagnosis method based on the feature reconstruction (FR) method and convolutional neural networks (CNN). This method can achieve high-precision fault diagnosis using single-phase stator current signals from motors as the diagnostic objects. First, the FR method effectively removes the impact of high-frequency noise, supply frequency, and its harmonics from the current signals, while also highlighting subtle fault feature signals to a certain extent. Second, a CNN suitable for learning the characteristics of the current signals was constructed. Through feature extraction, learning, and classification of the current signal samples processed by the FR method, a diagnostic method with a high classification accuracy was obtained. Visualization techniques were used to present the final diagnosis results intuitively. The experimental results demonstrated the highest diagnostic accuracy and average diagnostic accuracy of the proposed method in diagnosing rolling bearing fault types, with an average diagnostic accuracy of approximately 99% for actual faulty bearing samples.

Intelligent fault diagnosis method of rolling bearing based on multi-source domain fast adversarial network

Fault diagnosis of rolling bearings is among the most crucial links in the prognostic and health management of bearings. To solve the problem of single-source domain transfer learning that cannot adapt well to the target domain, a transfer diagnosis method based on multi-source domain fast adversarial network (MSDFAN) is proposed. First, signals from all domains are input into a common subnetwork of fast neural networks to reduce the complexity and network running time of neural networks. Secondly, several adversarial networks are constructed as domain specific feature extractors and then use Higher-order Moment Matching to reduce distribution differences between A and B domains. The two experimental cases of rolling bearing support the effectiveness and superiority of the proposed MSDFAN.

Bearing fault damage degree identification method based on SSA-VMD and Shannon entropy–exponential entropy decision

Aiming at the problem that the weak fault signal of rolling bearing is affected by background noise and the weak fault signal itself leads to the difficulty in extracting fault features, a weak fault diagnosis method of rolling bearing based on sparrow search algorithm-variational mode decomposition (SSA-VMD) and Shannon entropy–exponential entropy decision is proposed. Firstly, the failure energy ratio of the original signal is acquired to judge the bearing failure. Secondly, the original time-domain signal is decomposed by the VMD optimized by SSA-VMD to obtain the Intrinsic Mode Function (IMF) component, and the kurtosis and correlation coefficient are normalized and fused. The fusion parameter ratio ( RV) is used to filter the IMF component, and the filtered component is reconstructed to achieve the noise reduction effect. The reconstructed signal is subjected to Hilbert transform to obtain the envelope spectrum of the vibration signal, and the fault type of the bearing can be judged. Finally, the entropy of the reconstructed signal is input into the model based on entropy-multilayer forward neural network (MFNN) to identify the degree of bearing fault damage. The effectiveness of the method is verified by using the experimental data of different fault types of intermediate shaft bearings in Shenyang Aerospace University and the self-built experimental data of outer ring fault detachment evolution. The results show that the fault energy ratio of the original signal is more conducive to judging whether the bearing has a fault than the reconstructed signal. The bearing fault type diagnosis method based on SSA-VMD and parameter fusion screening can effectively identify fault characteristic frequency and its frequency doubling of the inner and outer rings of rolling bearings. The entropy values of different bearing damage signals have different distribution regions, which verify the effectiveness of the bearing fault damage identification method based on entropy–MLP judgement.

Fault diagnosis method of rolling bearing based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined with Long Short-term Memory Networks

Fault diagnosis method of rolling bearing based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined with Long Short-term Memory Networks

Bearing Fault Diagnosis Based on ICEEMDAN Deep Learning Network

Bearing fault diagnosis has evolved from machine learning to deep learning, addressing the issues of performance degradation in deep learning networks and the potential loss of key feature information. This paper proposes a fault diagnosis method for rolling bearing faults based on ICEEMDAN combined with the Hilbert transform (ICEEMDAN-Hilbert) and a residual network (ResNet). Firstly, the collected fault vibration signals are classified as fault samples and randomly sampled with a fixed length. The IMF components obtained by decomposing the bearing fault vibration signals using ICEEMDAN are able to maximize the restoration of fault vibrations. Then, the IMF components are transformed from one-dimensional time-domain signals to two-dimensional time-frequency domain images using Hilbert transformation. The RGB color images can be directly used in deep learning models without the need for manual labeling of a large amount of data, thereby avoiding the loss of key feature information. The ResNet network incorporates the attention mechanism (CBAM) structure for the precise extraction of fault features, enabling a more detailed classification of fault features. Additionally, the residual network effectively addresses the problem of performance degradation in multi-layer network models. Finally, transfer learning is applied in the deep learning network by freezing the training layer parameters and training the fully connected layer. This effectively solves the problem of insufficient data in real operating conditions, which hinders deep training of the model, while also reducing the training time. By combining the ResNet network with the convolutional block attention module (CBAM) structure, the model completes the recognition and training of time-frequency images for rolling bearing faults. The results demonstrate that the ResNet with CBAM model has strong fault feature extraction capabilities, achieving higher accuracy, 7–12% higher than other conventional network models, and exhibiting superior diagnostic performance compared to other deep learning models.

A modular fault diagnosis method for rolling bearing based on mask kernel and multi-head self-attention mechanism

Data-driven methods have been applied in fault diagnosis. However, in practical engineering, workers are more concerned with the real-time health status of bearings. And it is difficult to complete the effective training of diagnostic models with insufficient labeled fault data. Therefore, this paper proposes a modular method based on a mask kernel and multi-head self-attention mechanism for rolling bearing fault diagnosis. First, the proposed method divides the diagnosis into two modules of status detection and fault recognition. The approach of sharing one backbone for both modules simplifies the optimization process. The method combines the translation invariance of the convolution kernel and the mask attention mechanism of the transformer by computing the local self-attention and superimposing the partial local attention by the mask to ensure the integrity of the information. Finally, a zero-shot training method is proposed to embed the query into the model to achieve cross-distribution fault diagnosis of bearings. The experiments on the data sets of Case Western Reserve University and machinery fault simulator are implemented to diagnose the bearings. The results show that the proposed method can obtain higher diagnostic accuracy and computational efficiency than the existing methods and can be valid for scenarios with cross-condition diagnosis or imbalanced samples.

Intelligent fault diagnosis method for rolling bearing using WMNRS and LSSVM

A weighted multi-neighborhood rough set (WMNRS) algorithm is designed to resolve the issue in which the neighborhood radius must be adjusted iteratively and cannot be automatically determined in the neighborhood rough set. This algorithm combined with the least squares support vector machines (LSSVM) is used for analyzing rolling bearing condition monitoring data; consequently, an intelligent fault diagnosis method is proposed. Specifically, the time-domain and frequency-domain features are extracted from the collected vibration signals to construct an original feature set. The WMNRS algorithm is then applied to screen the primary sensitive components from the constructed feature set. Finally, an optimized LSSVM is utilized to recognize the fault types. The developed method is validated on a public dataset and a measured rolling bearing dataset. The results demonstrate that the method achieves excellent diagnostic performance. Furthermore, the proposed method has some supremacy regarding running time.

A zero-shot learning fault diagnosis method of rolling bearing based on extended semantic information under unknown conditions

A zero-shot learning fault diagnosis method of rolling bearing based on extended semantic information under unknown conditions

VMD–RP–CSRN Based Fault Diagnosis Method for Rolling Bearings

In response to the problems of low accuracy and poor noise immunity of the traditional fault diagnosis method for rolling bearing fault diagnosis due to the complex and variable operating conditions of rolling bearings and the large noise interference during bearing signal acquisition, a rolling bearing fault diagnosis model based on VMD–RP–CSRN is proposed. Firstly, the initial feature extraction of the bearing signal is carried out by variational modal decomposition (VMD), which is then converted into a two-dimensional image with fault features by recurrent plot (RP) coding, and then the feature images are input to a channel split residual network (CSRN) for feature extraction and fault classification. In order to verify the accuracy and noise immunity of the proposed method for the diagnosis of bearing faults under complex working conditions, experiments on the selection of parameters in the CSRN model were conducted on the bearing dataset of Jiangnan University, and experiments on the diagnosis of bearing faults under complex working conditions and noise immunity of CSRN were carried out and compared with other commonly used methods. The proposed bearing fault diagnosis method based on VMD–RP–CSRN combines VMD and RP to retain the fault features in the original signal to the maximum extent and stress the hidden features in the signal. The proposed channel split operation realizes the extraction of hidden features by selecting the main operating channel of the three-channel feature image, and makes more fault features participate in the feature extraction of the diagnosis model. The experimental results demonstrate that the proposed method is at least 1.2% better than the comparison method, and has better noise immunity. In addition, experiments on the fault diagnosis capability of the model with different data set sizes and the diagnosis of variable speed bearing data by the model show that the proposed method has better generalization performance and diagnosis capability.

Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network

When the rolling bearing fails, the fault features contained in bearing vibration signal are easily submerged by fortissimo noise interference signals, and have obvious non-stationary and nonlinear properties. This means that it is extremely challenging to acquire useful bearing fault features and identify bearing fault patterns effectively by traditional diagnosis methods. To more efficiently learn bearing fault information and improve bearing fault diagnosis accuracy, this research proposes a new intelligent fault diagnosis method for rolling bearing based on variational mode extraction (VME) and an improved one-dimensional convolutional neural network (I-1DCNN). Firstly, a new adaptive signal processing method named VME is employed to handle the collected bearing vibration signals with the aim of obtaining the desired mode component and removing the noise interference information. Meanwhile, the extracted mode components are randomly divided into the training set, validation set and test set. Then, the training set and validation set are input into the proposed I-1DCNN model for training, where the proposed I-1DCNN model may not only learn the discriminant features intelligently, but also boost the computational efficiency and alleviate the problem of over-fitting by incorporating the early stopping method and self-attention mechanism into the traditional one-dimensional convolutional neural network (1DCNN). Finally, the test set is input into the well-trained I-1DCNN to realize the automatic identification of different fault types of rolling bearing. The effectiveness of the suggested method is illustrated by analyzing two experimental data sets. In addition, by comparing with other representative methods, the superiority of the proposed method is testified in bearing health condition identification.

A fault diagnosis method of rolling bearing based on improved deep residual shrinkage networks

Aiming at the problem of signal distortion caused by deep residual shrinkage network (DRSN) in the noise reduction process, improved deep residual shrinkage network (IDRSN) are proposed and applied to rolling bearing fault diagnosis under noise backgrounds. Firstly, we design an improved pseudo-soft threshold function (IPSTF) to eliminate the signal distortion caused by the soft threshold function(STF). Then, a pseudo-soft threshold block (PSTB) and an adaptive slope block (ASB) are proposed to construct an improved residual shrinkage building unit (IRSBU) for setting the optimal threshold and slope adaptively. Finally, the method is applied to rolling bearing fault diagnosis in two different operating conditions under noise backgrounds. The results show that the proposed method has higher accuracy and robustness than the existing methods.

A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Fault diagnosis method for rolling bearing based on VMD and improved SVM optimized by METLBO

Fault diagnosis method for rolling bearing based on VMD and improved SVM optimized by METLBO

A novel intelligent fault diagnosis method of rolling bearings with small samples

It’s a challenging work to diagnose faults from the measured vibration signals automatically and efficiently under small samples. A new intelligent fault diagnosis method of rolling bearing with small samples is proposed based on structural similarity generative adversarial network (SSGAN) and improved MobileNetv3 convolutional neural network (IMCNN). Firstly, the wavelet transform (WT) is performed on the signal to obtain a wavelet 2D image with time–frequency characteristics. Then, SSGAN is constructed to obtain high-quality generated samples for expanding the small training sets. Finally, the improved MobileNetv3 convolutional neural network (IMCNN) is proposed to extract feature information of the extended samples by using the self-focus mechanism instead of the original lightweight focus mechanism, and the classification results are acquired for fault recognition. The experimental results show that the proposed SSGAN-IMCNN method can effectively extend the small samples and automatically detect the rolling bearing faults with high classification accuracy.

A novel transfer learning fault diagnosis method for rolling bearing based on feature correlation matching

As one of the mainstream transfer learning methods, correlation alignment (CORAL) has been widely applied in fault diagnosis field and has achieved certain achievements. However, CORAL ignores the differences between domains in the matching process, which makes it difficult to measure the discrepancies between domains accurately. To compensate the shortcomings of the CORAL, this paper proposes a new feature correlation matching (FCM) method, and further it is applied as the objective function to propose a deep feature correlation matching network (DFCMN). The FCM method focuses on both first-order feature correlation and second-order feature correlation of the source and target domains, which measures the discrepancies between different domains more comprehensively and accurately. With the powerful feature mapping capability of neural network, the DFCMN can improve the feature similarity in different domain centers while reducing the discrepancies of feature distribution between different domains, so as to obtain more reliable shared features and improve the cross-work-conditions diagnosis accuracy. The effectiveness of the proposed method is verified through multiple transfer tasks utilizing public rolling bearing data sets.

Composite Fault Diagnosis of Rolling Bearing Based on Chaotic Honey Badger Algorithm Optimizing VMD and ELM

In order to effectively extract the characteristic information of bearing vibration signals and improve the classification accuracy, a composite fault diagnosis method of rolling bearing based on the chaotic honey badger algorithm (CHBA), which optimizes variational mode decomposition (VMD) and extreme learning machine (ELM), is proposed in this paper. Firstly, aiming to solve the problem that the HBA optimization process can easily fall into local optimization and slow convergence speed, sinusoidal chaotic mapping is introduced to improve HBA, and the advantages of CHBA are verified by 23 benchmark functions. Then, taking the Gini index of the square envelope (GISE) as the fitness function, the VMD is optimized with CHBA to obtain the optimal number of modes K and the quadratic penalty factor. Secondly, the first four IMF components with the largest GISE values are selected, and the IMF components are grouped by the “Systematic Sampling Method (SSM)” to calculate the signal energy to form the fault feature vector. Finally, taking the classification error rate as the fitness function, the feature vector is input into the ELM model optimized by CHBA to classify and identify different types of faults. Through experimental analysis, and compared with BP, ELM, GWO-ELM, and HBA-ELM, this method has better diagnosis results for composite faults, and the accuracy of fault classification can reach 100%, which provides a new way to solve the problem of composite fault diagnosis.

Fault diagnosis of rolling bearings based on multi-scale deep subdomain adaptation network

This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.

A compound fault diagnosis method of rolling bearing based on wavelet scattering transform and improved soft threshold denoising algorithm

The vibration signal of faulty rolling bearing of rotating machine carries a large amount of information reflecting its fault categories. However, compound fault features are easily mixed together, and can cause missed diagnosis and misjudgment, which is still a challenging task in mechanical fault diagnosis. A compound fault detection method using wavelet scattering transform (WST) and an improved soft threshold denoising algorithm is proposed to extract compound faults in bearings. First, the wavelet scattering transform is used to calculate the original scattering coefficients from vibration signals. Second, the improved soft threshold denoising algorithm is applied to obtain the renewable scattering coefficients, which are further employed to reconstruct the denoising signals. Third, process the envelope spectrum analysis on the denoising signal to extract fault features. Finally, both the simulations and experiments in associate with comparison investigations proved that this method can effectively detect compound faults in bearings.

Fault Diagnosis Method of Rotating Machinery Based on Collaborative Hybrid Metaheuristic Algorithm to Optimize VMD

With the improvement of the complexity and reliability of mechanical equipment, it has been difficult for the commonly used variational modal decomposition method of vibration signal of rotating machinery to meet the current practical engineering requirements. In order to further improve the adaptability, processing efficiency, and robustness of rotating machinery fault diagnosis methods, a collaborative hybrid element heuristic to multiobjective optimization algorithm is introduced in this paper. Combined with variational modal decomposition (VMD) method, the fault diagnosis method of rolling bearing under complex working conditions is studied. This paper mainly uses a collaborative hybrid metaheuristic algorithm to improve the nondominated sorting genetic algorithm II (NSGA II) and multiobjective particle swarm optimization (MOPSO), which improves the convergence efficiency of multiobjective optimization method and solves the problem of uneven distribution of optimal solutions. Then, the improved multiobjective optimization algorithm is combined with VMD to solve the problem of parameter selection of the VMD method under complex working conditions of rotating machinery. At the same time, the variation relationship between various signal features and VMD decomposition results is compared and studied, and the features with good effect are taken as the objective function of the optimization algorithm; the ability of denoising and feature extraction of VMD in rotating machinery fault diagnosis is improved. In this paper, the proposed method is explored by using analog signals and experimental data of rolling bearings. Through comparison, the improvement of adaptive ability, operation speed, and robustness of the proposed method in rotating machinery fault diagnosis is verified.

Compound fault diagnosis of rolling bearing under variable speed based on generalized demodulation transformation and symplectic geometric mode decomposition

In this paper, a superior compound fault diagnosis method of rolling bearing under a variable speed is proposed, which is based on the generalized demodulation transformation and symplectic geometric mode decomposition. First, generalized demodulation transformation is performed on a time-varying non-stationarity compound fault signal to smooth the fault components by calculating the characteristic frequency, phase function, and theoretical fault frequency point. Then, a new signal decomposition method is applied to extract component signals of different frequencies from the smoothed fault components, and the frequency of each extracted component signal is obtained. Finally, the frequencies of the obtained fault components are compared with theoretical fault frequencies to perform decoupling and diagnosis of compound fault. The simulation and experimental analysis results show that the proposed method can effectively decouple different fault components of compound fault without using the order tracking, providing a superior solution for the diagnosis of compound fault under a time-varying rotational speed.