Fault Diagnosis Method Of Rolling Research Articles

WCNN-RSN: a novel fault diagnosis method for rolling bearing using multimodal feature fusion

Abstract Fault diagnosis of rolling bearings is significant for the safe operation of engineering equipment. Many intelligent diagnostic methods have been successfully developed. However, it is often susceptible to noisy environments. Therefore, the paper proposes a rolling bearing fault diagnosis method based on multimodal information fusion in time and time–frequency domains by combining an improved 1D-convolutional neural network (CNN) with ResNet50 wavelet improved CNN-ResNet (WCNN-RSN). The algorithm employs the Multi-Head Attention (MHA) mechanism to complementarily fuse fault features in different scales, achieving fault diagnosis by fully extracting fault features. The experimental results show that the diagnostic effect of WCNN-RSN is better than that of the comparison methods under noise interference, which proves that the proposed method possesses good anti-noise and generalization ability.

Fault diagnosis method of rolling bearing based on noise reduction enhanced multi-frequency scale network

Fault diagnosis method of rolling bearing based on noise reduction enhanced multi-frequency scale network

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Abstract Considering the issue of capturing the local and global contextual information and enhancing the parallel capability of bearing fault diagnosis in variable load and noise environments, a fault diagnosis method of rolling bearing based on PE-DCM and ViT is proposed. Firstly, the one-dimensional vibration signal is converted into a two-dimensional time-frequency diagram by continuous wavelet transform in the data processing module, and the model can understand the characteristics of the vibration signal more comprehensively. Secondly, a pyramid exponential expansion convolution module is established to extract the local features of fault information. Then, the global features of the fault information are learnt through the ViT (Vision Transformer) network, and the adaptive multi-attention is used to dynamically adjust the attention weights according to the features of the input data so as to inhibit noise or unimportant information. Finally, the experimental verification is carried out by using Case Western Reserve University and self-made MFS-bearing data set. The experimental results show that the method can better reflect the powerful image classification ability of the ViT network and has better noise resistance and generalization compared with other fault diagnosis methods.

Refined composite multiscale slope entropy and its application in rolling bearing fault diagnosis

Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods.

Fault diagnosis method of rolling bearing based on MSSA-VMD algorithm

Abstract Early fault signals of rolling bearings are affected by environmental noise, which makes it challenging to extract fault characteristics. In this paper, a bearing fault diagnosis method based on Sparrow Search Algorithm (SSA) and Variable Modal Decomposition (VMD) is proposed to achieve adaptive signal decomposition and feature extraction. First, the fusion shock index is constructed based on the envelope spectrum pulse factor and correlation coefficient to measure the shock fault components. Then, the VMD parameters are adaptively optimized by the improved sparrow search algorithm MSSA, and the fusion shock index is established as the optimal modal component selection method of the fitness function. Finally, the envelope spectrum of the signal after noise reduction is analyzed to extract the fault characteristic frequency. By analyzing the bearing outer ring fault signal and inner ring fault signal under strong noise, the results show that this method can adaptively determine the mode number and penalty factor of VMD, effectively extract the fault signal characteristics, and realize rolling bearing fault diagnosis.

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.