Bearing Fault Identification Research Articles

Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs

Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs

Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions

Because of the complex operating environment of high-end industrial machinery, rolling bearing is generally operated at fluctuating working conditions such as variable speeds or loads, thus enables fault feature information is not obvious. That said, bearing fault identification under fluctuating working conditions are recognized as a very challenging problem. Deep learning blazes a valid route to address this issue by right of strong self-learning performance. Nevertheless, the performance of traditional deep learning model will degrade in the face of the fluctuating data with a sharp rising and heavy external interference. Therefore, to overcome this limitation, this study proposes a novel method named deep order-wavelet convolutional variational autoencoder (DOWCVAE) to identify bearing faults under fluctuating speed conditions, which can improve feature learning ability of a plain convolutional variational autoencoder (CVAE). Within this approach, an improved energy-order analysis with frequency-weighted energy operator (FWEO) is firstly presented to convert the raw time-domain vibration signal into the resampled angle-domain signal to relieve the influence of speed fluctuating and acquire the enhanced order spectrum data. Afterwards, wavelet kernel convolutional block (WKCB) with anti-symmetric real Laplace wavelet (ARLW) is constructed to extract the latent feature information closely related to equipment states from the enhanced order spectrum data via the stacked way layer by layer, which is capable of further promoting learning performance of overall network model and improve its generalizability. In addition, a high-efficiency intelligent optimization algorithm termed as multi-objective gray wolf optimizer (MOGWO) is introduced for choosing automatically optimal wavelet parameters of DOWCVAE model and avoiding negative impact posed by artificially adjusting parameter. Ultimately, the learned latent features are loaded to the softmax classifier to achieve automatic identification of different bearing health states and provide comprehensive diagnosis result. The analysis results from two experiment cases testify the effectiveness of our approach. Quantitatively, average identification accuracy of the proposed approach can reach 99% above, which shows its competitive advantages and is more satisfying as compared to some representative deep learning methods.

Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method.

Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy.

Data-driven fault diagnosis methods for rotating machinery have developed rapidly with the help of deep learning methods. However, traditional intelligent fault diagnosis methods still have some limitations in fault feature extraction and the latest object detection theory has not been applied in fault diagnosis. To this end, a fault diagnosis method based on a sparse short-term Fourier transform (SSTFT) and object detection theory is developed in this paper. First, a sparse constraint is introduced in time-frequency analysis to improve the time-frequency resolution of the model without cross-term interference and proximal gradient descent (PGD) is adopted to quickly and effectively optimize the model to obtain a high-quality time-frequency representation (TFR). Second, a fault diagnosis model based on a region-based convolutional neural network (RCNN) is built; the model can extract multiple regions that can characterize fault features from the TFR. This process avoids the interference of irrelevant vibration components and improves the interpretability of the fault diagnosis model. Finally, multicategory rolling bearing fault identification is realized. The effectiveness of the proposed method is validated by simulation signals and bearing experiments. The results indicate that the proposed method is more effective than existing methods.

Bearing fault diagnosis method based on refined composite multiscale fuzzy entropy

In this paper, a new fuzzy C-mean (FCM) clustering method based on refined composite multiscale fuzzy entropy (RCMFE) is proposed and applied to the bearing fault diagnosis of rotating machinery. The RCMFE adds multiscale processing, which can not only characterize the complex changes of time sequences at several different scales but also solve the problems of information loss caused by the process of coarse granulation and large fluctuation of entropy value when the scale becomes large. This method uses RCMFE to extract fault features, that are inputted into the Fuzzy C-mean clustering algorithm to achieve rolling bearing fault identification. Comparing the method with Multiscale Fuzzy Entropy (MFE) and Composite Multiscale Fuzzy Entropy (CMFE), the method used was experimentally proven to have better diagnostic results.

Cubic spline interpolation-based refined composite multiscale dispersion entropy and its application to bearing fault identification

As a powerful tool, dispersion entropy (DE) has good capability to measure the irregularity and complexity of nonlinear systems, so it is extensively utilized in the field of structural health monitoring. However, traditional multiscale dispersion entropy (MDE) will suffer from down-sampling as the scale factor increases, which compresses some intrinsic feature information, so that the representation ability of MDE for nonlinear system is significantly restrained. Aiming at this problem, a new method called cubic spline interpolation-based refined composite multiscale dispersion entropy (CSIRCMDE) is proposed. In this frame, the coarse-graining series are regarded as knots to calculate the interpolative points in different sub-sections. Then, the obtained coarse-graining samples and interpolative samples are used to construct interpolation series to capture the potential vibration characteristics and constrain the down-sampling phenomenon. Finally, the first coarse-graining point is gradually shifted back to make multiple groups of interpolation series, and the entropy values are rectified by calculating mean pattern probabilities at the same scale. Through these steps, CSIRCMDE can grasp sufficient feature information to reduce entropy errors and overcome the dependence on sample size compared with traditional algorithms, which is demonstrated by simulation and noise signals. Furthermore, two types of bearing damage experiments prove that CSIRCMDE has good capability to characterize the bearing states, therefore, based on the proposed algorithm, the classification model that can fully identify different bearing faults is established.

Modeling of instantaneous angular speed variation of large clearance cylindrical roller bearing with local defects in rolling element

The instantaneous angular speed (IAS) has been gradually applied to bearing fault identification. The IAS of the fault bearing have periodic disturbance by bearing dynamic modeling and fault simulation experiment in some studies. However, few of them has involved the local characteristics of IAS periodic disturbance, especially the large clearance bearing, which may lead to misdiagnosis. In this paper, a bearing rolling element fault dynamic model is proposed by considering the contact stiffness between the rolling elements and races based on the Hertzian contact theory. Moreover, A fault with 0.25 mm width and 0.2 mm depth was seeded on the roller of the bearing using the laser to verify the validity of simulation results. The results show that the periodic impulse is observed in the IAS, which is mainly due to the sudden change of torque when the defect of the rolling element contacts the raceway, especially in the load area.

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network

With the development of information technology, it has become increasingly important to use intelligent algorithms to diagnose mechanical equipment faults based on vibration signals of rolling bearings. However, with the application of high-performance sensors in the Internet of Things, the complexity of real-time classification of multichannel, multidimensional sensor signals is increasing. In view of the need for intelligent methods for fault diagnosis methods of mechanical equipment, the generalization ability of fault diagnosis models also needs to be further strengthened. In this context, in order to make fault diagnosis intelligent and efficient, a bearing fault diagnosis method based on spectrum map information fusion and convolutional neural network (CNN) is proposed. First, short-time Fourier transform (STFT) is used to analyze the multichannel vibration signal of the rolling bearing and obtain the frequency domain information of the signal over a period of time. Second, the information fusion is converted into two-dimensional (2D) images, which are input into CNN for training, and the bearing fault identification model is obtained. Next, the frequency domain information of each signal is converted into a 2D spectrum map, which is used as a CNN training dataset to train a bearing fault identification model. Finally, the diagnostic model is validated using the existing datasets. The results show that the accuracy of fault diagnosis using the proposed bearing is greater than 99.4% and can even reach 100%. The proposed method considerably reduces the workload of the diagnosis process, with strong robustness and generalization ability.

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

In this work, a novel bearing fault identification scheme making use of deep learning has been proposed. Initially, the raw vibration signal is passed through a time-varying filter based empirical mode decomposition (TVF-EMD) to obtain different modes. Filter parameters of TVF-EMD are optimized by a newly developed optimization algorithm i.e. ameliorated African vulture optimization algorithm The Kernel estimate for mutual information has been considered as the fitness index for the developed optimization algorithm. The mode having the least value of fitness index is known as a prominent mode from which sensitive features representing different bearing conditions are extracted. These extracted features help in preparing the data matrix which is further utilised to build fuzzy-based classification models. The results obtained revealed that the linguistic hedge neuro-fuzzy classifier obtained maximum performance with the least computational time. The comparison of the developed method has also been done with other classification models viz., KNN, SVM, ELM and random forest that revealed the superiority of the developed method.

Intelligent Roller Bearing Fault Diagnosis in Industrial Internet of Things

Advanced research studies on industrial Internet of things require effective feature extraction and accurate machinery health state evaluation. For roller bearing, a well-known mechanical component most extensively used in the industry, its running status directly affects the operation of the entire machinery and equipment. For intelligent fault diagnosis of roller bearing, the selection of the intrinsic mode function (IMF) modes in approaches of ensemble empirical mode decomposition (EEMD)/variational mode decomposition (VMD) becomes a tricky problem. To solve this problem, this study proposed an efficient scheme on roller bearing fault diagnosis that combines the refined composite multivariate multiscale sample entropy (RCMMSE) with different classifiers. Firstly, the synthetic noise signals are introduced to compare the effectiveness of the multiscale sample entropy (MSE) and the RCMMSE models. Secondly, the random noise signals are used to compare the performance of EEMD and VMD methods, where the envelope spectrum characteristics of fault signals are well described. Moreover, EEMD/VMD methods are utilized to decompose the roller bearing vibration signals into various modes to get the entropy values. Finally, the obtained RCMMSE is adopted as a feature vector and subsequently employed as an input of support vector machine, random forest, and probabilistic neural network models to conduct roller bearing fault identification. The extensive experimental results prove that this proposed scheme performs well and the classification accuracy of VMD-RCMMSE is higher than EEMD-RCMMSE.

Fault Identification of Rolling Bearing with Variable Speed Based on Generalized Broadband Mode Decomposition and Det

Fault Identification of Rolling Bearing with Variable Speed Based on Generalized Broadband Mode Decomposition and Det

Initial Fault Diagnosis of Rolling Bearing Based on Second-Order Cyclic Autocorrelation and DCAE Combined With Transfer Learning

Under actual working conditions, the initial faults of rolling bearings are difficult to be predicted effectively because of the lack of prior fault knowledge, weak fault information, and strong noise interference. How to enhance the fault characteristics while removing the vibration signal noise of rolling bearings has become the key challenge to the field of early fault diagnosis of rolling bearings. Based on the above problems, a rolling bearing initial fault diagnosis model based on second-order cyclic autocorrelation (Soca) and deep auto-encoder (AE) combined with transfer learning (TL) was proposed to improve the overall performance of rolling bearing fault identification. First, the Soca is used to estimate the second-order cyclic statistics of the vibration signal, which can better highlight the periodic pulse characteristics of rolling bearings and suppress random noise to a certain extent. After that, the dataset containing the valuable health state information of the rolling bearing is input into the denoising and contraction auto-encoder (DCAE). The advantages of denoising AEs (DAEs) and contraction AEs are fully utilized to extract the initial fault characteristics of rolling bearings. Finally, the balanced distribution adaptation (BDA) is used to reduce the distribution difference and class spacing of the transferable features extracted from the original datasets and the target datasets, and the initial fault diagnosis of the target rolling bearing is diagnosed according to the feature knowledge of the source rolling bearing. In three experimental datasets, six TL experiments were carried out to verify that the Soca-Denoising auto-encoder, Contraction auto-encoder and Transfer learning (DCT) model can effectively enhance the initial fault characteristics of rolling bearings, and has higher diagnostic accuracy and robustness compared with the existing initial fault diagnosis methods of rolling bearings. This method has a good application prospect in the field of early fault diagnosis of rolling bearings.

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment, a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network (IADRSN) is proposed. Firstly, the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets. Then, a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established. To improve the ability of fault feature extraction of the model, the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer. To further improve the anti-noise ability of the model, the first layer of the model was set as the Drop-block layer. Finally, the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results. The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Rolling bearings are the key components of rotating machinery. Incipient fault diagnosis of bearing plays an increasingly important role in guaranteeing normal and safe operation of rotating machinery. However, because of the high complexity of the fault feature extraction, the incipient faults of rolling bearings are difficult to diagnose. To solve this problem, this paper presents a new incipient fault intelligent identification method of rolling bearings based on variational mode decomposition (VMD), principal component analysis (PCA), and support vector machines (SVM). In the proposed method, the bearing vibration signals are decomposed by using VMD, and a series of intrinsic mode functions (IMFs) with different frequencies are obtained. Then, the energy and kurtosis values of each IMF are calculated to reveal the intrinsic characteristics of the vibration signals in different scales. Finally, all energy and kurtosis values of IMFs are processed via PCA and subsequently fed into SVM to achieve the bearing fault identification automatically. The effectiveness of this method is verified through the experimental bearing data. The verification results indicate that the proposed method can effectively extract the bearing fault features and accurately identify the bearing incipient faults, and outperform the two compared methods obviously in identification accuracy and computation time.

Depth Prototype Clustering Method Based on Unsupervised Field Alignment for Bearing Fault Identification of Mechanical Equipment

In the process of mechanical equipment fault diagnosis, it is difficult to obtain enough labeled samples due to the changeable operating conditions, complex working environment, and the limitation of measuring equipment. Therefore, a prototype clustering method based on unsupervised domain alignment is proposed for fault detection and diagnosis of mechanical equipment under unmarked samples. First, the relative maximum mean discrepancy index is constructed to map the data collected from different devices to the same high-dimensional space for feature alignment. Then, cluster the output and calculate the prototype for each category. Compared with the classification directly using the softmax function, the prototype clustering method can correct the local possible misjudged points through the overall average distribution. By minimizing the global distance error, prototypes obtained by clustering in the target domain are matched with prototypes of tagged samples in the source domain and, finally, realize unsupervised state recognition. Six experiments are designed to verify the effectiveness of the proposed method. The accuracy of the proposed method is 1%–2% higher even when compared to one of the most advanced fault diagnosis methods available. Also, it can be found from the results of t-stochastic neighbor embedding (SNE) visualization that the proposed method has higher knowledge transfer capability.

Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN

Due to the structure of rolling bearings and the complexity of the operating environment, collected vibration signals tend to show strong non-stationary and time-varying characteristics. Extracting useful fault feature information from actual bearing vibration signals and identifying bearing faults is challenging. In this paper, an innovative optimized adaptive deep belief network (SADBN) is proposed to address the problem of rolling bearing fault identification. The DBN is pre-trained by the minimum batch stochastic gradient descent. Then, a back propagation neural network and conjugate gradient descent are used to supervise and fine-tune the entire DBN model, which effectively improve the classification accuracy of the DBN. The salp swarm algorithm, an intelligent optimization method, is used to optimize the DBN. Then, the experience of deep learning network structure is summarized. Finally, a series of simulations based on the experimental data verify the effectiveness of the proposed method.

Bearing fault diagnosis via generalized logarithm sparse regularization

Bearing fault is the most common causes of rotating machinery failure. Therefore, accurate bearing fault identification technique is of tremendous significance. Vibration monitoring has been used widely and the key is to reconstruct the fault shock components from monitoring signals with noise. Sparse representation is proved to be a promising method to extract the repetitive transient component from noisy signals. However, common sparse representation methods suffer from the shortcomings of insufficient reconstruction accuracy or the underestimation of amplitude. To overcome these drawbacks, we proposed a new non-convex penalty called generalized logarithm(G-log) penalty, which enhances the sparsity and reduces noise disturbance. Although the proposed penalty is not convex itself, the cost function of sparse representation is preserved to be convex by parameter setting, hence the convex optimization algorithms are capable of being applied to obtain the global minimum. In addition, the k-sparsity method is used to decide the regularization parameter adaptively. Simulation and experiments verify that the proposed G-log method performs well in bearing fault diagnosis and generates more reconstruction accuracy compared to other sparse representation methods.

Bearing fault identification based on stacking modified composite multiscale dispersion entropy and optimised support vector machine

Bearing vibration signals have strong non-linear characteristics and contain significant interference noise, however, entropy is an effective means of rationalising the complexity of time-series data and provides a feasible solution for fault feature extraction, classification, and recognition. A novel stacking modified composite multiscale dispersion entropy (SMCMDE) algorithm is proposed to overcome the deficiency of multiscale dispersion entropy. Firstly, the signal is preprocessed by filtering. Then, the stacking operation was applied to the filtered signal, and the standard deviation of the coarse-grained sequence was calculated to replace the original mean operation for correction. The analysis of simulation signals shows that the proposed algorithm has certain advantages over other entropy algorithms. Subsequently, an intelligent bearing fault identification method based on SMCMDE and equilibrium optimiser-based support vector machine (EO-SVM) is proposed. The results from tests on bearings show that the proposed method can not only extract the sensitive features of bearing faults, but also has higher accuracy and stability than other methods.