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Rolling bearings are widely used in machinery and equipment, how to extract the feature and identify the fault of rolling bearings have become essential issues for ensuring the safe operation of rotation machinery. The fault signals of rolling bearings present nonlinear and non-smooth characteristics which introduce certain challenges to extracting the fault signal. To completely extract the features of signal, this study proposes a novel feature extraction method based on quadratic wavelet packet energy entropy (QWPEE) and t-distributed stochastic neighbor embedding (t-SNE) for bearing fault identification. Firstly, the vibration signals are divided into various node signals by wavelet packet decomposition (WPD). Next, the wavelet packet energy entropy (WPEE) of each node signal in the last layer is extracted to form the initial QWPEE feature vector. After that, the original QWPEE feature data are fused by the t-SNE method to obtain the final feature data set. Finally, the support vector machine (SVM) is employed to identify the states of the bearing fault. The experiments of bearing fault are created to ascertain the performance of the proposed methodology. The experimental outcomes demonstrate that the proposed methodology is efficacious in accurately identifying states of rolling bearing fault. [ABSTRACT FROM AUTHOR]

The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings. So, an end-to-end bearing fault diagnosis method: GMSCNN, a bearing fault diagnosis method based on Gram Matrix (GM) and Multi scale Convolutional Neural Network (MSCNN), is proposed in this paper. In this method, first, GM is used to reduce the noise of the collected vibration signals; Secondly, MSCNN is used for feature extraction, and the characteristics of vibration signals at different frequencies and time scales can be captured by the convolutional kernels of different scales; thirdly, two feature enhancement branches are added, utilizing the undenoised vibration signal as input, to enrich and diversify features while enhancing the model's expressive and generalization capabilities; Finally, the experimental analysis was conducted on two bearing datasets to indicates that the noise robustness of GMSCNN is strong. [ABSTRACT FROM AUTHOR]

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.)

The remaining useful life (RUL) prediction of rolling bearings is crucial for optimizing maintenance schedules, reducing downtime, and extending machinery lifespan. However, existing multi-channel feature fusion methods do not fully capture the correlations between channels and time points in multi-dimensional sensor data. To address the above problems, this paper proposes a multi-channel feature fusion algorithm based on a hybrid attention mechanism and temporal convolutional networks (TCNs), called MCHA-TFCN. The model employs a dual-channel hybrid attention mechanism, integrating self-attention and channel attention to extract spatiotemporal features from multi-channel inputs. It uses causal dilated convolutions in TCNs to capture long-term dependencies and incorporates enhanced residual structures for global feature fusion, effectively extracting high-level spatiotemporal degradation information. The experimental results on the PHM2012 dataset show that MCHA-TFCN achieves excellent performance, with an average Root-Mean-Square Error (RMSE) of 0.091, significantly outperforming existing methods like the DANN and CNN-LSTM. [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]

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]

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]

Copyright of Light Industry Machinery is the property of Light Industry Machinery 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.)

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]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

The multiscale fluctuation dispersion entropy algorithm (MFDE) is widely used to extract the characteristics from a variety of complex nonlinear signals, including bearing signals, due to its excellent performance to quantify the uncertainties of complex nonlinear systems. However, limited by the classification number and coarse-graining process, the periodic impulses generated by the defect point cannot be effectively detected by MFDE, restraining the characterization abilities of entropy features and resulting in undesirable diagnosis results for bearing faults. To overcome the disadvantages of MFDE, an amplitude-based multiscale dispersion entropy (AMDE) is proposed in this paper. The AMDE utilizes the phase scale factor to calculate multiple groups of amplitude difference series that contain different amplitude information. As such, the amplitude compression caused by the large-scale factor in traditional coarse-graining process is avoided, and the calculated entropy features not only characterize the irregularity of the whole signal but also reflect the changes of the impulse components. Afterwards, the perception range and the sensibility of AMDE are expanded and enhanced for amplitude variation, and the coarse-graining process and Gaussian reference are used to obtain multi-dimensional reversed entropy features. Combining those steps, the amplitude-based multiscale reverse dispersion entropy (AMRDE) algorithm is proposed. Finally, the capability of the proposed algorithm to track the amplitude variation and fluctuation is successfully demonstrated by analyzing noisy signals and amplitude-modulated signal. Meanwhile, the features extracted from bearing signals demonstrated that it is effective to use AMRDE to represent the health conditions of rolling bearing. Therefore, the entropy metric calculated by AMRDE can be the useful indicator in the fields of mechanical equipment fault diagnosis, structural health monitoring, and so on. [ABSTRACT FROM AUTHOR]

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.)

To address the difficulty in extracting the characteristics of combined failure of rolling bearings, a novel fault identification method, one-dimensional mixed binary pattern (1D-MBP), is proposed. Firstly, regarding that variance can better highlight the partial weak failure information of sequence than median, the binarization of vibration signals based on one-dimensional local binary pattern (1D-LBP) is carried out with variance as criterion. Meanwhile, binarization sequence is converted to decimal sequence as the local conversion signal (LCS). Secondly, considering that failure information of rolling bearings is reflected in partial and global texture, the paper proposed the method of gaining global weighted mean value by weighting partial mean value with partial energy. Binarize vibration signals based on one-dimensional global binary pattern (1D-GBP) with obtained global weighted mean value as criterion and convert the obtained binarized sequence into decimal as the global conversion signal (GCS). Thirdly, signals are reconstructed by obtained LCS and GCS to get 1D-MBP signal. Finally, combined failure types of bearing can be identified from 1D-MBP signals based on TEAGER energy spectrum (TES). The result indicates that the 1D-MBP not only can effectively control noise components in vibration signal but also can precisely identify the combined failure types of rolling bearings. [ABSTRACT FROM AUTHOR]

Aiming at the prediction model of bearing performance degradation based on recurrent neural network (RNN) and its variants ignores the feature spatial correlation, and cannot effectively handle long time series data, this paper proposes a rolling bearing performance degradation prediction model based on temporal graph convolutional neural network (T-GCN). For non-stationary and nonlinear characteristics of vibration signals, this paper introduces a rolling bearing feature evaluation method based on multiscale dispersion entropy (MDE) to better characterize time series. To effectively solve the spatial correlation problem between samples and features, this paper uses the topological structure of a path graph to build a graph model and combines gated recurrent unit (GRU) and graph convolutional neural network (GCN) to build a T-GCN prediction model. Finally, this article established a rolling bearing fault prediction experimental platform and validated it using the University of Cincinnati public dataset. The experiment shows that compared with GRU, GCN, and LSTM models, the RMSE and the MAE evaluation indicators based on the T-GCN model have decreased by 6 % to 28 % and 11 % to 28 %, respectively, which suggests that the T-GCN model has a higher prediction accuracy and a better model fitting goodness. [ABSTRACT FROM AUTHOR]

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 Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

A rolling bearing fault diagnosis method based on Markov transition field (MTF) and the pyramid cascade multidimensional convolutional neural network (PC-MDCNN) model is proposed to address the problems of poor fault diagnostic performance and generalization performance. These problems are caused by the complex and variable working conditions of rolling bearings and the difficulty in collecting fault samples. First, the one-dimensional vibration signal is transformed into two-dimensional images using MTF. Then, a pyramid cascade multidimensional feature extraction module (PC-MDFEM) is introduced to reduce model parameters and extract fault information of the feature maps comprehensively by focusing on different dimensions of the feature maps and combining convolutional layers at different levels. Afterward, PC-MDFEM is applied to the convolutional neural network to construct a PC-MDCNN model. Finally, the MTF feature maps are input into the proposed model for training, and the proposed model is compared with other fault diagnosis models. Experimental results demonstrate that the proposed method has strong classification performance, generalization performance and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is crucial in the application of deep belief networks. The improved deep belief networks are proposed based on Hard swish activation function and the improved whale optimization algorithm. The optimized diagnosis model is applied to weak fault diagnosis of bearings. The experimental results show that this method has faster convergence speed and anti-noise performance under the interference of −5 db noise, and effectively improves the adaptive feature extraction ability of the model and the accuracy of fault diagnosis. Under different conditions, the average accuracy reached 97.8% and 97.6%, with good generalization performance. [ABSTRACT FROM AUTHOR]

To address the challenges of accurately diagnosing few-shot fault samples obtained from rolling bearings under variable operating conditions, as well as the issues of black box nature and delayed feedback to guide fault handling in intelligent diagnostic models, this paper proposes an interpretable multi-domain meta-transfer learning method. Firstly, vibration monitoring data of rolling bearings under different operating conditions are collected, and time–frequency domain features are extracted to construct multi-channel one-dimensional temporal samples as inputs. A multi-domain meta-transfer learning framework based on deep convolutional neural networks is then built to perform few-shot learning with multiple tasks under different operating conditions. The output results are reverse-reconstructed through a fusion hierarchical class activation mapping, and the feature maps are assigned different weights to obtain saliency maps corresponding to the inputs, thus improving the interpretability of the output results. Finally, the dataset of bearing vibration data under time-varying rotational speed conditions is used to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve accurate fault diagnosis results under variable operating conditions with few-shot samples, and the diagnosis results can be fed back to the input for decision-making, enhancing the interpretability of the model. Compared with other models, it also demonstrates better robustness and accuracy. [ABSTRACT FROM AUTHOR]

Recent advancements in applications of deep neural network for bearing fault diagnosis under variable operating conditions have shown promising outcomes. However, these approaches are limited in practical applications due to the complexity of neural networks, which require substantial computational resources, thereby hindering the advancement of automated diagnostic tools. To overcome these limitations, this study introduces a new fault diagnosis framework that incorporates a tri-channel preprocessing module for multidimensional feature extraction, coupled with an innovative diagnostic architecture known as the Lightweight Ghost Enhanced Feature Attention Network (GEFA-Net). This system is adept at identifying rolling bearing faults across diverse operational conditions. The FFE module utilizes advanced techniques such as Fast Fourier Transform (FFT), Frequency Weighted Energy Operator (FWEO), and Signal Envelope Analysis to refine signal processing in complex environments. Concurrently, GEFA-Net employs the Ghost Module and the Efficient Pyramid Squared Attention (EPSA) mechanism, which enhances feature representation and generates additional feature maps through linear operations, thereby reducing computational demands. This methodology not only significantly lowers the parameter count of the model, promoting a more streamlined architectural framework, but also improves diagnostic speed. Additionally, the model exhibits enhanced diagnostic accuracy in challenging conditions through the effective synthesis of local and global data contexts. Experimental validation using datasets from the University of Ottawa and our dataset confirms that the framework not only achieves superior diagnostic accuracy but also reduces computational complexity and accelerates detection processes. These findings highlight the robustness of the framework for bearing fault diagnosis under varying operational conditions, showcasing its broad applicational potential in industrial settings. The parameter count was decreased by 63.74% compared to MobileVit, and the recorded diagnostic accuracies were 98.53% and 99.98% for the respective datasets. [ABSTRACT FROM AUTHOR]

Addressing the issues that single-source information cannot comprehensively reflect the operational status of equipment, redundant features fail to diagnose effectively, and information fusion is challenging, this study explores a novel method of feature extraction, selection, and fusion for wind turbine rolling bearing from the perspective of multi-viewpoint and multi-source heterogeneous information fusion, aimed at diagnosing inner ring crack faults of rolling bearings. Initially, based on the failure mechanism of rolling bearings, a multi-source and multi-domain feature set is constructed from both signal processing and data-driven perspectives. By investigating the latent relationships among feature variables, a random forest model is utilized to optimize and reduce the dimensionality of the multi-source feature set. Subsequently, an improved PCR6 method is employed for decision-level fusion of the random forest classification results, thereby facilitating fault extraction, dimensionality reduction, and fault classification of wind turbine bearings from multi-source and multi-viewpoint perspectives. The results indicate that the constructed multi-source and multi-viewpoint feature set enhances the model's recognition performance (with a 5% increase in accuracy), and the fusion of features and decision layers further improves the accuracy of fault diagnosis. In cases where single-feature set classification is erroneous, the proposed decision-layer fusion model's classification probability can provide accurate fault classification. [ABSTRACT FROM AUTHOR]

Due to the insufficient sensing field of the temporal convolutional networks (TCN), the key degradation information of the bearing is often ignored, which results in poor prediction of the remaitning useful life (RUL) of bearings. Moreover, the long-term dependence problem of long short-term memory (LSTM) may not be well solved with the increase of data volume and sequence length. Therefore a new prediction method based on Bidirectional temporal convolutional network and Long short-term memory (Bi-TCN-LSTM) was proposed. Firstly, the multi-sensor data was normalized and fused, and then the Bi-TCN-LSTM was used for data feature extraction and deep learning, in which the convolutional attention mechanism (CAM) was introduced into the TCN module, and the three gates of the LSTM were simplified into one gate. It effectively accelerated the learning speed of the prediction model and improved the accuracy of the prediction model. The IEEE PHM 2012 bearing dataset was used to carry out the RUL prediction experiments. The results show that compared with other advanced prediction models, the Bi-TCN-LSTM method has relatively lower prediction error and better performance. [ABSTRACT FROM AUTHOR]

In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault feature extraction and diagnosis methodology that seamlessly integrates an improved Fourier decomposition method (FDM), singular value decomposition (SVD), and maximum second-order cyclostationary blind convolution (CYCBD). Initially, the FDM is employed to meticulously decompose the bearing fault signals into numerous signal components. Subsequently, a comprehensive weighted screening criterion is formulated, aiming to strike a balance between multiple indicators, thereby enabling the selective screening and reconstruction of pertinent signal components. Furthermore, SVD and CYCBD techniques are introduced to carry out intricate processing and envelope demodulation analysis of the reconstructed signals. Through rigorous simulation experiments and practical rolling bearing fault diagnosis tests, the method's noteworthy effectiveness in suppressing noise interference, enhancing fault feature information, and efficiently extracting fault features is unequivocally demonstrated. Furthermore, compared to traditional time–frequency analysis methods such as EMD, EEMD, ITD, and VMD, as well as traditional deconvolution methods like MED, OMEDA, and MCKD, this method exhibits significant advantages, providing an effective solution for diagnosing rolling bearing faults in environments with strong background noise. [ABSTRACT FROM AUTHOR]

Since bearing fault signal in complex running status is usually characterized as nonlinear and non-stationary, it is difficult to extract accurate affluent features and achieve effective fault identification via conventional signal processing tools. In this article, a rolling bearing fault diagnosis technique based on variational mode decomposition and weighted multidimensional feature entropy fusion is proposed to address this issue, which is mainly composed of three procedures. First, the original signal undergoes the variational model decomposition. Next, the signal features are extracted by weighted multidimensional feature entropy as the input of the diagnosis model. Finally, the classification is performed by a convolutional neural network. The method is applied in simulation and experimental analysis. The experimental results show that the proposed method, which demonstrates strong immunity to noise and robustness, can more effectively and adaptively extract the fault features of rolling bearings and achieve the goal of identifying the rolling bearing fault category and damage degree under variable operating conditions. Meanwhile, this approach exhibits superior accuracy and identification performance to some similar entropy-based hybrid approaches referred to in this article, with a promising prospect in industrial application. [ABSTRACT FROM AUTHOR]

When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME's center frequency ω and penalty factor α, enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels. [ABSTRACT FROM AUTHOR]

Rotor bearing health is crucial for ensuring the operational stability of rotating equipment. Deep learning-based fault diagnosis methods have achieved widespread success due to their superior fault identification capability. However, conventional deep learning methods that rely on large quantities of data are not feasible for most important mechanical equipment since obtaining fault data is difficult. To address this problem, we propose channel attention siamese networks (CASN) with metric learning for intelligent bearing fault diagnosis with extremely small samples. First, in the feature learning phase, pairs of sample inputs are constructed, and feature extraction is performed by a shared encoder. Then, in the disparity learning phase, the differences between features of sample pairs are mapped as metric distances. Based on the metric distance between the unlabeled and labeled data, the fault type of the unlabeled data can be predicted in the test phase. The experimental results show that CASN achieves over 97% accuracy when the sample size is extremely small. In addition, even under the conditions of noise interference and signal transmission distortion, our model still has reliable diagnostic ability. [ABSTRACT FROM AUTHOR]

Traditional fault diagnosis models often overlook the interconnections between segments of vibration data, resulting in the loss of critical feature information. Additionally, the vibration signals of rolling bearings exhibit non-linear behaviors during operation. Therefore, an efficient fault diagnosis model tailored for rolling bearings is proposed in this paper. In the proposed model, the 1D vibration signals are first preprocessed using ensemble empirical mode decomposition (EEMD). This technique generates multiple intrinsic mode functions (IMF) as individual nodes. The percentage distance between each node is calculated using the Wasserstein distance (WD) to capture the relationships between nodes and use it as the edge weights to construct a node graph. This unique approach enhances the transformation of 1D vibration signals into a node graph representation, preserving important information. An improved multi-head graph attention network (MGAT) model is established to extract features and perform classification on the node graph. This MGAT model effectively utilizes the relationships between nodes and enhances the accuracy of fault diagnosis. The experimental results demonstrate that the proposed method achieves higher accuracy compared to similar models while requiring less processing time. The proposed approach contributes significantly to the field of fault diagnosis for rolling bearings and provides a valuable tool for practical applications. [ABSTRACT FROM AUTHOR]

To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time–frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time–frequency spectra of the enhanced signals, obtaining the optimal time–frequency spectra. Finally, all sample data are transformed into the optimal time–frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method. [ABSTRACT FROM AUTHOR]

The vibration signal of rolling bearing is interfered by the coupling of other various components and environment, which brings challenges to effective feature expression and the establishment of accurate predictor. A fault diagnosis method via 2D feature map of cyclic spectral coherence (CSCoh) after denoising and multi-scale convolutional neural network (MSCNN) under different conditions is proposed. The original vibration signal is processed by DTCWPT and threshold denoising, and a 2D feature map extraction model based on CSCoh is established to reflect hidden periodic characteristics. The feature differences can be highlighted and the coupling interference can be further eliminated. The multi-scale convolution kernels are proposed to build a parallel structure, and a fusion structure is followed. Benefit by the structures in MSCNN, the local and global features of 2D feature map can be preserved comprehensively, the learning ability and diagnostic performance can be improved. Finally, the verification experiments of rolling bearings in different positions under different operating conditions are carried out, and the experimental results show the proposed method has better generalization ability and comprehensive accuracy of more than 95% under different conditions. [ABSTRACT FROM AUTHOR]

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.)

Although pure time-domain features have the advantages of fast extraction speed and clear physical meaning, the diagnostic accuracy is slightly inferior to other methods. To solve this problem, a new bearing feature extraction method based on the time subsequence (BOTS) is proposed, which combines word package model and time subsequence. First, the sliding window is used to slide in the vibration signal to obtain multiple continuous and non-stationary time series, which are regarded as a document. For each time series, multiple continuous subsequences of fixed length are randomly intercepted to obtain the time-domain or frequency-domain characteristics of subsequences. Then, the random forest algorithm is used to count the class votes of all subsequences in each time series, and a dictionary is constructed based on the class votes. Finally, the dictionary is used as a new feature and input into the random forest classifier for training and learning. A variety of experiments are carried out using the bearing data provided by the SQI-MFS experimental platform of Wuxi Innovation Center of SIEMENS China Research Institute, Southeast University and Institute of Mechanical Failure Prevention Technology. The experiments show that the features extracted by BOTS+ wavelet packet energy method have higher recognition.

Abstract Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

Weak bearing fault feature extraction (FFE) research has previously focused on bearing fault signal of transient impulse extraction whereas higher harmonic feature extraction of fault feature frequency aspects are relatively less. While traditional envelope demodulation method has certain limitations to capture the rolling bearing fault characteristic frequency of higher harmonic. To this end, combining adaptive chirp mode decomposition (ACMD), improved maximum correlation kurtosis deconvolution (IMCKD), and 1.5-dimensional Teager energy cyclo-stationary spectrum (1.5-DTECS), in the current work we propose a three-stage defect detection system. The performance of the proposed method is evaluated by analyzing the three-stage FFE (STFFE) of multiple kinds of rolling bearing fault data. The findings reveal that the three-stage fault detection approach successfully suppresses noise, highlights the fault impact, and extracts the higher order harmonics of the bearing defect characteristic frequency more effectively. The research contributes to the field of bearing fault high harmonic component extraction and provides guidance on techniques related to the extraction of bearing impulse characteristic. [ABSTRACT FROM AUTHOR]

To solve the problems that the neighbor distribution information of the samples was ignored for local linear embedding during mining the local manifold structure, and the default samples had the same importance in the dimensionality reduction process, leading to the inconspicuous feature extraction, the weighted local linear embedding based on shared neighbors (SN-WLLE) was proposed for bearing fault diagnosis. The cosine distance was adopted to divide the sample neighborhood in SN-WLLE. The sample neighborhood pair similarity was calculated by Jaccard coefficient to evaluate the sample shared neighbor information, and the local structure mining was modified by combining six neighbor distributions of the sample to improve the accuracy of the k-nearest neighbor reconstruction of multiple shared neighbors. The consistency of the sparse distribution between the sample and neighbors was evaluated to obtain the importance index of sample from the perspective of multi-manifolds, and the information was maintained in the low-dimensional space to extract accurate identification features. The complete bearing fault diagnosis model was constructed by combining KNN classifier. The bearing dataset of Case Western Reserve University and the bearing dataset of the laboratory test platform were used to analyze visual evaluation, quantitative clustering evaluation, fault identification accuracy evaluation and robustness evaluation. The results show that the F-value of SN-WLLE is maintained above 10~8, and the average fault identification accuracy can reach the minimum value of 0.973 4, which not only has good intra-class compactness and inter-class separability, but also has low sensitivity to the nearest neighbor parameter k. [ABSTRACT FROM AUTHOR]

Due to the difficulty in dealing with non-stationary and nonlinear vibration signals using the single decomposition method, it is difficult to extract weak fault features from complex noise; therefore, this paper proposes a fault feature extraction method for rolling bearings based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods. CEEMDAN was used to decompose the signal, and the signal was then screened and reconstructed according to the component envelope kurtosis. Based on the kurtosis of the maximum envelope spectrum as the fitness function, the sparrow search algorithm (SSA) was used to perform adaptive parameter optimization for VMD, which decomposed the reconstructed signal into several IMF components. According to the kurtosis value of the envelope spectrum, the optimal component was selected for an envelope demodulation analysis to realize fault feature extraction for rolling bearings. Finally, by using open data sets and experimental data, the accuracy of envelope kurtosis and envelope spectrum kurtosis as a component selection index was verified, and the superiority of the proposed feature extraction method for rolling bearings was confirmed by comparing it with other methods. [ABSTRACT FROM AUTHOR]

An innovative deep-learning-based model, namely, deep anomaly detection with the same probability distribution (DADSPD) is proposed to improve the accuracy of anomaly detection (AD) of rolling bearings driven only by normal data. First, the main framework of feature extraction based on a residual network was established, and a three-layer encoder structure was used to extract multidimensional features. Second, a new loss function based on the same probability distribution is designed, and the function of its probability distribution is to complete the training of the model by calculating the similarity between the outputs. Subsequently, the vibration data were preprocessed using wavelet and envelope analysis, and the processed data are converted into two-dimensional image signals and used as the input of the DADSPD. Finally, the model is verified on three sets of run-to-failure experimental datasets of rolling bearing. The results demonstrate that the proposed DADSPD model reaches more than 99%, which indicates that the DADSPD model has a high fault early warning and AD capability. [ABSTRACT FROM AUTHOR]

The quality of rolling bearings determines the safety of mechanical equipment operation, and bearings with more precise structures are prone to damage due to excessive operation. Therefore, cross domain fault diagnosis of bearings has become a research hotspot. To better improve the accuracy of bearing cross domain fault diagnosis, this study proposes two models. One is a cross domain feature extraction model constructed using a mixed attention mechanism, which recognizes and extracts high-level features of bearing faults through channel attention and spatial attention mechanisms. The other is a bearing cross domain fault diagnosis model based on multi-layer perception mechanism. This model takes the feature signals collected by the attention mechanism model as input to identify and align the differences between the source and target domain features, facilitating cross domain transfer of features. The experimental results show that the mixed attention mechanism model has a maximum accuracy of 97.3 % for feature recognition of different faults, and can successfully recognize corresponding signal values. The multi-layer perception model can achieve the highest recognition accuracy of 99.5 % in bearing fault diagnosis, and it can reach a stable state when it iterates to 26, and the final stable loss value is 0.28. Therefore, the two models proposed in this study have good application value. [ABSTRACT FROM AUTHOR]

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To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment, it is necessary to obtain more accurate and effective fault classification features for bearings. Accordingly, a bearing fault classification feature extraction method based on multisensor fusion technology and an enhanced binary one-dimensional ternary pattern (EB-1D-TP) algorithm were proposed in this study. First, an optimal equalization weighting algorithm was established to realize high-precision fusion of bearing signals by introducing an optimal equalization factor and determining the theoretical optimal equalization factor value. Second, an enhanced binary encoding method similar to balanced ternary encoding was developed, which increases the difference between the different fault features of the bearing. Finally, the new sequence obtained after encoding was used as the input to a support vector machine to classify and diagnose the faults of the rolling bearing. The experimental results show that the algorithm can significantly improve the accuracy and speed of rolling-bearing fault classification. Combining fusion-encoding features with other intelligent classification methods, the classification results were improved. • An optimized equalization random weighting algorithm is proposed. • A "Zero Removal" enhancement processing technique is proposed. • A new encoding method more suitable for one-dimensional signal is constructed. [ABSTRACT FROM AUTHOR]

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.)