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The intelligent prediction of bearing remaining useful life (RUL) plays a critical role in bearing maintenance. Therefore, it is particularly significant to accurately estimate the RUL of bearings in order to ensure the reliability and safety of mechanical systems. And deep learning techniques have been successfully applied in the RUL prediction. However, there are unresolved problems of information loss during feature extraction and hardly effectively extracting spatio-temporal sequence information during bearing degradation process for the convolutional neural networks. To solve the problem, this paper proposes a RUL prediction framework based on stacked residual convolutional autoencoder and spatio-temporal convolutional bidirectional gated recurrent unit. The method adopts continuous wavelet transform technology to convert the acquired raw vibration signals into two-dimensional time–frequency images, constructs a deep network using stacked residual convolutional networks to extract feature information at different levels, and learns the spatio-temporal information in the time series information in the past and future states through spatio-temporal convolutional bi-directional gated recurrent units to more accurately predict the remaining service life of rolling bearings. In experimental verification, by comparing with existing RUL prediction methods and utilizing the PHM2012 and XJYU-SY public datasets, the superiority and effectiveness of our proposed method were well validated. The experimental results indicate that the proposed RUL prediction approach exhibits excellent performance in terms of accuracy and generalization. [ABSTRACT FROM AUTHOR]

Demands for improved fuel efficiency in automobiles and other vehicles have led to smaller, lighter power transmission device which result in high surface contact stress and a thin oil film, which in turn tends to cause the temperature of rolling bearings to rise. The most common temperature measurement method is to touch a thermocouple against the inner and outer rings, and this method has been used for many years. However, the method using thermocouples can only measure temperatures in a limited range near the measurement point. The authors applied the Seebeck effect, a phenomenon in which an electromotive force is generated when different metals are connected and a temperature difference is applied to bearings, to a method of measuring bearing raceway temperatures called the dynamic thermocouple method. In the dynamic thermocouple method, the average value of each contact points between the different metals generates the emf (electromotive force), so the temperature rise of all the each rolling elements in contact becomes the average value, and the exact point of temperature rise is not clear. Therefore, all but one rolling element was changed to electrically insulating zirconia balls. With this method, the contact points between many different metals became one, making it possible to identify the locations of temperature rises on the raceway surface. This method makes it possible to directly measure the temperature change of the raceway. The results of temperature measurements of the raceway surface using two types of bearings with different raceway accuracy showed a clear difference of temperature. The bearing with a poor raceway accuracy showed a temperature rise in the unloaded zone, and slippage was observed when the behavior of the rolling element was checked with a high-speed camera. Furthermore, in bearings with good raceway accuracy, the temperature of the raceway surface remained almost constant even in the non-load zone. By using the dynamic thermocouple method and observing the rolling elements with a high-speed camera, it was possible to correlate the bearing temperature rise with the behavior of the rolling elements. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty in obtaining the eigenfrequency of the vibration component of rolling bearing faults in a strong background noise environment and the problem of extraction efficiency, the adaptive chirp mode decomposition (ACMD) combined with Improved maximum second-order cyclostationary blind deconvolution (ICYCBD) fault feature extraction algorithm is proposed. Firstly, to improve the signal-to-noise ratio, the original signal is adaptively decomposed using the ACMD method, and the optimal components are selected based on the principle of maximizing the correlation gini coefficient index. Secondly, to improve the accuracy of parameter setting and extraction efficiency, an improved CYCBD method is proposed to estimate the cyclic frequency set of CYCBD using the proposed enhanced energy harmonic product spectrum (EEHPS) method for the optimal components, the envelope spectrum peak factor index is improved by proposing the envelope spectral period pulse factor (EPPF) index, and the filtering length of the CYCBD is selected adaptively using the step search to obtain the optimized filtered signal. Finally, the envelope spectrum analysis is carried out to extract the fault information accurately. The simulation signals and experimental data show that the method can quickly and accurately extract the fault characteristics of rolling bearings under strong background noise, and the comparison with other methods shows the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Rolling bearings are the critical components of rotating mechanical equipment, and it is more important to fault diagnosis and recognition of the rolling bearings. Multivariate variational mode decomposition (MVMD) with one dimensional residual network (1D ResNet) is proposed to fault diagnosis and subtle feature recognition of the rolling bearings. Intrinsic modal components are extracted to further signal process under different operational conditions to the segmentation of signal components and the feature reconstruction. The average success accuracy rate for the ten types of rolling bearing faults (normal, ball fault, inner race fault, outer race fault with different damage degree) exceeds 99.32 %. MVMD-1D ResNet with the advantage of fault recognition of rolling bearings is validated by comparing to other algorithms (1D ResNet, 1D CNN and KNN). MVMD-1D ResNet model has great potential to condition monitoring and subtle feature recognition with limited sample sizes of the rolling bearings. [ABSTRACT FROM AUTHOR]

Due to the mutual coupling between sub-faults in the case of compound faults in rolling bearings, coupled with external noise, energy attenuation during information transmission, etc., the acquired fault information is usually very weak and complex. This greatly increases the difficulty of extracting features from bearing compound faults. To further enhance the accuracy of fault feature extraction and achieve precise identification of rolling bearing compound faults, this paper presents an innovative compound fault diagnosis method that integrates variable step-size multiscale weighted Lempel-Ziv complexity (VMW-LZC) and intrinsic time-scale decomposition (ITD). First, considering that the traditional Lempel-Ziv complexity (LZC) method can only extract single-dimensional fault information without thoroughly exploiting fault characteristics, we optimize the coarse-graining process of proper rotation components (PRCs) after ITD using a variable step-size multiscale strategy. Additionally, LZCs obtained from variable step-sizes in each scale are fused and reconstructed using a weighted method. Meanwhile, multiple variable step-size multiscale LZCs are combined into a new signal evaluation index, VMW-LZC. In addition, with the new signal evaluation index VMW-LZC, the optimal PRC which is best representative of fault characteristic information is chosen. Furthermore, frequency spectrum of autocorrelation function of optimal PRC is used to identify multiple faults of bearings. To exemplify the efficiency of presented method, a comparison has been made among the optimal PRC chosen by the methods with VMW-LZC, traditional LZC (T-LZC), multiscale LZC(M-LZC), and classical kurtosis as signal evaluation indexes. The result has indicated that fused signal evaluation index VMW-LZC can actualize more precise option of sensitive fault component signals and the proposed ITD-VMW-LZC method can do a more precise job in identifying a compound fault of bearings. [ABSTRACT FROM AUTHOR]

The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback–Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference. [ABSTRACT FROM AUTHOR]

To address the issue of low diagnostic accuracy caused by noise interference and varying rotational speeds in rolling bearings, a fault diagnosis method based on domain-conditioned feature correction is proposed for rolling bearings under complex working conditions. The approach first constructs a multi-scale self-calibrating convolutional neural network to aggregate input signals across different scales, adaptively establishing long-range spatial and inter-channel dependencies at each spatial location, thereby enhancing feature modeling under noisy conditions. Subsequently, a domain-conditioned adaptation strategy is introduced to dynamically adjust the activation of self-calibrating convolution channels in response to the differences between source and target domain inputs, generating correction terms for target domain features to facilitate effective domain-specific knowledge extraction. The method then aligns source and target domain features by minimizing inter-domain feature distribution discrepancies, explicitly mitigating the distribution variations induced by changes in working conditions. Finally, within a structural risk minimization framework, model parameters are iteratively optimized to achieve minimal distribution discrepancy, resulting in an optimal coefficient matrix for fault diagnosis. Experimental results using variable working condition datasets demonstrate that the proposed method consistently achieves diagnostic accuracies exceeding 95%, substantiating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved. [ABSTRACT FROM AUTHOR]

The traditional high-frequency resonance technology has two disadvantages: (1) The resonance frequency (RF) is obtained by using Fourier spectrum (FS) and the bandwidth is obtained based on experience, and the effect is often poor due to severe noise interference; (2) the separation of compound faults is not considered. After systematic research, a feasible fault detection methodology named enhanced adaptive high-frequency resonance technology is summarized and introduced, which is on the basis of power spectrum (PS) analysis, abbreviated as enhanced adaptive high-frequency resonance technique based on power spectrum (PS-EAHFRT), to realize the single fault detection and compound fault separation of bearing. Firstly, the theoretical values of various bearing fault modes are calculated to establish a database. Then, a series of spectral peaks are obtained by performing PS analysis, and each prominent spectral peak is considered as an alternative RF, that is, an alternative central frequency. Then, the bandwidth calculation formula of the bandpass filter is given, and the optimal bandwidth parameter is calculated by using the signal envelope kurtosis as a sparse measure. Finally, the original signal is denoised by the optimized band-pass filter, and then the filtered signal is demodulated by envelope PS analysis, and compared with the theoretical value in the library, and then the fault category is obtained. Simulated and real-world bearing cases are used to demonstrate the effectiveness and advantages of PS-EAHFRT. It is demonstrated that the PS-EAHFRT can effectively realize the bearing single fault detection and the compound fault separation. Moreover, the lower bound of the frequency interval in which this method can achieve bearing composite fault separation is explored. [ABSTRACT FROM AUTHOR]

In conditions of multi-fault coupling, varying loads and speeds, as well as noise interference, bearing vibration signals present various complex issues, leading to difficulties in feature extraction and the need for a large number of training samples for diagnostic methods. This paper designs a multi-fault coupling experiment for rolling bearings under varying load and speed conditions and proposes a new fault diagnosis method that uses the power spectrum of the AR model and a convolutional neural network to diagnose complex multi-faults in rolling bearings. It takes the original vibration signal as input, uses the AR model to convert the time-domain signal into a power spectrum, and then classifies it using a convolutional neural network. To test the performance of the AR model power spectrum convolutional neural network, this method was compared with some fault diagnosis methods. The results show that this method can achieve higher diagnostic accuracy under varying loads and speeds, and requires fewer training samples. In addition, the noise resistance of this method is also superior to other fault diagnosis methods. [ABSTRACT FROM AUTHOR]

To solve the problems of insufficient feature extraction of the current methods under small sample conditions and information loss in the process of signal transformation from different domains, a bearing fault diagnosis method based on multi-domain feature fusion and heterogeneous networks under small sample conditions is proposed. The method firstly designs the data preprocessing module to transform and combine the raw vibration signals into multi-domain signals by Fast Fourier Transform and Gram Angle Field, which provides rich feature conditions for the subsequent feature extraction. Then, heterogeneous branch networks are designed for different domain signals used in low-dimensional feature extraction in the high-dimensional nonlinear space of fault data. When the inputs or intermediate processes of one branching network are interfered by the outside world, another branching network would play the role of error correction, which enhances the fault-tolerance of the proposed method. Next, in order to enhance the critical feature extraction capability of the heterogeneous network, the Location-Aware Channel Enhancement Block (LACEB) is designed. LACEB learns the unique weights for different channels and different locations in the feature map by adaptively adjusting the dynamic factors and feature location parameters. Further, the memory unit in the global feature extraction module is used to learn the context information of each time step, and the dependency between the global features and the local features is effectively established. Finally, in order to prevent the model from falling into local optimal, a learning rate adaptive optimization algorithm is designed to optimize the model training process. A variety of strictly comparative experiments are tested on the CWRU dataset and the MFS dataset, the results show that the proposed method is capable of better performing fault diagnosis tasks in different environments and devices. [ABSTRACT FROM AUTHOR]

In order to solve the difficult problem of weak fault feature extraction of rolling bearing, a fault diagnosis method of rolling bearing based on acoustic emission characteristic parameters and waveform flow analysis was proposed, based on waveform analysis of acoustic emission signal under complex transmission path. Firstly, the state of the rolling bearing was preliminarily judged by the acoustic emission characteristic parameter TAFI (time arrival feature index). Secondly, the fault diagnosis of rolling bearing was carried out by using experience graph analysis and distribution graph analysis. Finally, the waveform flow of acoustic emission signal of faulty bearing was screened and reconstructed by kurtosis criterion to extract fault information. In order to verify the effectiveness of this method, a rolling bearing fault simulation test and an aero-engine intermediate bearing simulation test were carried out, and the acoustic emission signals of typical rolling bearing faults were obtained, and the data were processed and analyzed by the established method. The results show that the TAFI image of acoustic emission characteristic parameters presents regular bars, which can preliminarily determine that the rolling bearing is in fault state. The impact number of energy, count value, amplitude and count of the faulty bearing is higher than that of the healthy bearing, which can effectively identify the fault characteristics of the rolling bearing. The typical faults of rolling bearing can be judged by analyzing acoustic emission waveform flow. [ABSTRACT FROM AUTHOR]

Recently, bearing fault diagnosis based on transfer learning (TL) has been a hot topic, which has attracted widespread interest due to its ability to adapt bearing fault datasets with different feature distributions. However, existing research suffer from low diagnosis efficiency and poor generalization capabilities. Therefore, an improved parallel residual network‐domain adaptation (IPRN‐DA) method for bearing fault diagnosis is proposed in this paper, which is to address these challenges. Firstly, a parallel residual block (PRB) is designed to extract critical features that can fully characterize the original signals without significantly increasing the model parameters and more attention is paid to them. Secondly, a hybrid attention mechanism (HAM) is constructed to adaptively integrate channel and spatial features to enhance fault feature information. Finally, multikernel maximum mean discrepancy (MK‐MMD) is employed to measure the distribution difference between the source and target domains in advanced feature extraction and predicted label spaces, implementing high‐precision bearing transfer diagnosis. Rolling bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU) are used to validate the effectiveness of the presented method. Experimental results illustrate that the algorithm can extract domain‐invariant features for different cross‐domain diagnosis tasks and thus improve fault diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Rolling bearings are indispensable components of many engineering machinery, especially rotating machinery. If rolling bearing faults are not diagnosed promptly, it may cause huge economic losses. Bearing fault diagnosis can avoid catastrophic accidents, ensure the reliability of equipment operation, and reduce maintenance costs. Existing intelligent bearing fault diagnosis methods have fast diagnosis speeds and excellent fault recognition capabilities, which is not feasible for most important mechanical devices because of the difficulty in obtaining fault samples for training. To tackle this problem, a two-stage bearing fault diagnosis method without fault sample training based on fault feature knowledge is proposed. In the first stage, a fault detection vector is constructed based on signal statistical indicators. The Mahalanobis distance of the feature vector between online signals and historical normal signals serves for anomaly detection. In the second stage, based on the bearing fault knowledge, envelope spectrum fault indicators are proposed to form diagnosis vectors. By calculating the similarity between the diagnosis vector and the present fault label, the probability of different fault types will be obtained. Three experimental analyses show that the method is effective in detecting early faults and achieves high fault identification accuracy. The above results advantageously prove that the method can be used for fault diagnosis without fault sample training, and has the possibility of practical application. [ABSTRACT FROM AUTHOR]

In rotating machinery, the condition of rolling bearings is paramount, directly influencing operational integrity. However, the literature on the fault evolution of rolling bearings in their nascent stages is notably limited. Addressing this gap, our study establishes an innovative nonlinear dynamic model for early fault evolution of rolling bearings based on collision impact. Firstly, considering the fault evolution characteristics, the influence of the rolling element and fault structure, the dynamic model of early fault evolution between the rolling element and the local fault is established. Secondly, according to the Hertzian contact deformation theory, a nonlinear dynamic model of rolling bearings expressed as mass-spring is established. Thirdly, the energy contribution method is used to integrate the fault evolution model and the nonlinear dynamic model of the rolling bearing. A nonlinear dynamic model of early fault evolution of the rolling bearing is proposed by using the Lagrangian equation. Comparing the simulation results of the nonlinear dynamic with the experimental results, it can be seen that the numerical model can effectively predict the evolution process and vibration characteristics of the fault evolution of rolling bearings in the early stage. [ABSTRACT FROM AUTHOR]

Feature enhancement is important in mechanical equipment fault diagnosis. A limited set of characteristic parameters is insufficient for diagnosing bearing signals with multiple fault types. The presence of noise increases the difficulty of extracting fault features from images. To address the challenge of diagnosing rolling bearing faults in complex environments, this study presents an enhanced weighted image fusion framework aimed at enhancing fault features within the images, which enables accurate diagnosis of bearing faults using a limited number of features. The proposed method encompasses four distinct stages. In the first stage, a symmetrized dot pattern method is employed to transform one-dimensional time-series data into two-dimensional images, visualizing the signal in a 2D format. In the second stage, image binarization and an improved weighted fusion method are utilized to simplify subsequent processing and enhance the image features. The third stage involves extracting the image's contrast and maximum singular value to improve the Canberra distance calculation. Finally, the enhanced Canberra distance is used for classifying bearing faults. Performance testing of the image feature enhancement is conducted on various datasets containing rolling bearings. Comparative experiments with alternative enhancement methods demonstrate the superiority of the proposed improved weighted image fusion framework. Comparative experiments with the original Canberra distance validate the effectiveness of the enhanced Canberra distance. Additionally, experiments conducted in noisy environments confirm the robustness of the proposed approach. Furthermore, the image feature enhancement method is applied to other bearing datasets, and the experimental results demonstrate its effectiveness in enhancing fault feature representation and achieving accurate diagnosis of rolling bearings. [ABSTRACT FROM AUTHOR]

The rolling bearing is an important supporting part of the spindle drive system of the computer numerical control machine tool. The defects of the bearing assembly affect the performance and work efficiency of the whole computer numerical control machine tool drive system. To investigate the difference in internal dynamic characteristics of bearings under the condition of defects, the dynamic characteristics of the defective bearings were investigated with 7002C angular contact ball bearings as the research object. Firstly, the dynamic model of bearing assembly with pitting defects was constructed by using finite element simulation software. Then, the simulation results and theoretical calculation results of the ball rotation speed of the bearing at different speeds were compared. Finally, the dynamic characteristics of the defects of the outer ring, the ball, and their compound defects were comprehensively analyzed. The research results show that the shear stress of composite defects is higher than that of the single defects, and the shear stress fluctuates sharply. Different fault states show the difference in vibration signals. Ball defects show little fluctuation, outer ring defects are stable, and compound defects are the most unstable and have the largest vibration amplitude. The ball' s rotational velocity exhibits irregular and inconsistent variations, and the ball defects, outer ring defects, and compound defects exhibit similar but slightly different periodicity and peak-valley delay on the velocity curve. The constructed finite element model can be used to study the dynamic characteristics of rolling bearings under different fault states and can provide some important references for the optimization design of bearings. [ABSTRACT FROM AUTHOR]

As an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing. [ABSTRACT FROM AUTHOR]

Rolling bearing is a critical component of machinery that has been widely applied in manufacturing, transportation, aerospace, and power and energy industries. The timely and accurate bearing fault detection thus is of vital importance. Computational data-driven deep learning has recently become a prevailing approach for bearing fault detection. Despite the progress of the deep learning approach, the deep learning performance is hinged upon the size of labeled data, the acquisition of which is expensive in actual implementation. Unlabeled data, on the other hand, are inexpensive. In this research, we develop a new semi-supervised learning method built upon the autoencoder to fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance. Compared with the state-of-the-art semi-supervised learning methods, this proposed method can be more conveniently implemented with fewer hyperparameters to be tuned. In this method, a joint loss is established to account for the effects of labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other well-established baseline methods. Specifically, nearly all emulation runs using the proposed methodology can lead to around 2%–5% accuracy increase, indicating its robustness in performance enhancement. [ABSTRACT FROM AUTHOR]

In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning–variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios. [ABSTRACT FROM AUTHOR]

Rolling bearings are crucial components of rotating machinery, and their health states directly affect the overall performance of the machinery. Therefore, it is exceedingly necessary to detect and diagnose bearing faults. Numerous bearing fault diagnosis methods have been successfully used for ensuring the safe operation of rotating machinery. However, in practical working environments, there is a considerable amount of noise, resulting in traditional methods incapable of achieving accurate fault diagnosis. This paper proposes a new multi-head attention residual network (MARNet) for rolling bearing fault diagnosis under noisy condition. MARNet optimizes residual units by simplifying multi-layer convolutions into a single-layer convolution and replaces the rectified linear unit (ReLU) function with the exponential linear unit (ELU) function to obtain a more appropriate activation function. Additionally, the multi-head attention mechanism is introduced into the residual block to capture correlation information between any two time sequences, enhancing the network's feature extraction capability. The effectiveness and superiority of the MARNet in noisy environments are demonstrated through conducting the two bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU). The experiment results show that the proposed method exhibits anti-noise characteristics and generalization capability compared with several up-to-date deep learning methods for fault diagnosis of rolling bearings. [ABSTRACT FROM AUTHOR]

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To address the issue of long training time and low efficiency in traditional rolling bearing fault diagnosis methods, a fault diagnosis method based on convolutional neural networks (CNN) and broad learning system (BLS) is proposed to realize fast and accurate end-to-end pattern recognition. A broad convolutional learning system (BCLS) is established by combining CNN and BLS, using CNN to extract signal features and BLS for classification to generate system output. BLS layers are integrated through residual learning to form a stacked broad convolutional learning system (SBCLS), which optimize the error between predicted outputs and real labels, thereby recognizing bearing fault patterns. Control experiments are set up to verify the proposed method. A comparative test with three BLS methods indicate that the proposed method offers superior diagnostic performance. In addition, when compared to several common fault diagnosis methods, the proposed method demonstrates higher accuracy and training efficiency, showing promise for intelligent fault diagnosis at the edge. [ABSTRACT FROM AUTHOR]

The cycle frequency and filter bandwidth are difficult to determine in the maximum second-order cyclostationary blind deconvolution (CYCBD) feature extraction. In this study, the frequency weighted energy correlation spectrum (FWECS) is introduced to improve the CYCBD and achieves the bearing fault feature extraction under low signal-to-noise ratio conditions. This method firstly obtains the periodic impact frequency by FWECS and constructs the cyclic frequency set. Secondly, an equal-step search strategy is designed to adaptively select the filter length using the maximum weighted harmonic significant index. Finally, the CYCBD is performed based on the optimized cyclic frequency and filter bandwidth. Bearing simulation and experimental data verification show that FWECS-CYCBD is more sensitive to the weak impact features in the fault signal under the circumstance that the priori information such as the cyclic frequency is unknown. Compared with methods such as minimum entropy deconvolution, improved maximum correlation kurtosis deconvolution and adaptive maximum second-order cyclostationary blind deconvolution, the proposed method is able to extract the frequency information of bearing fault features under low signal-to-noise ratio conditions. [ABSTRACT FROM AUTHOR]

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Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In rotating machinery, the condition of rolling bearings is paramount, directly influencing operational integrity. However, the literature on the fault evolution of rolling bearings in their nascent stages is notably limited. Addressing this gap, our study establishes an innovative nonlinear dynamic model for early fault evolution of rolling bearings based on collision impact. Firstly, considering the fault evolution characteristics, the influence of the rolling element and fault structure, the dynamic model of early fault evolution between the rolling element and the local fault is established. Secondly, according to the Hertzian contact deformation theory, a nonlinear dynamic model of rolling bearings expressed as mass-spring is established. Thirdly, the energy contribution method is used to integrate the fault evolution model and the nonlinear dynamic model of the rolling bearing. A nonlinear dynamic model of early fault evolution of the rolling bearing is proposed by using the Lagrangian equation. Comparing the simulation results of the nonlinear dynamic with the experimental results, it can be seen that the numerical model can effectively predict the evolution process and vibration characteristics of the fault evolution of rolling bearings in the early stage.