Rolling Bearing Fault Diagnosis Research Articles

The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network

In response to challenges associated with feature extraction and diagnostic models’ complexity in the early diagnosis of bearings’ faults, this paper presents an innovative approach for the early fault diagnosis of rolling bearings. This method combined concepts from frequency domain signal analysis with lightweight neural networks. To begin, vibration signals from rolling bearings were collected using vibration sensors, and the mean square value was utilized as an indicator for accurate early fault signal extraction. Subsequently, employing the fractional Fourier transform, the time domain signal was converted into a frequency domain signal, which provided more detailed frequency feature information. The fusion process combined amplitude frequency and phase frequency information, and was visualized as a Gram angle field map. The lightweight neural network Xception was selected as the primary fault diagnosis tool. Xception, a convolutional neural network (CNN) variant, was chosen for its lightweight design, which maintains excellent performance while significantly reducing model parameters. The experimental results demonstrated that the Xception model excelled in rolling bearing fault diagnosis, particularly when utilizing fused information datasets. This outcome underscores the advantages of combining information fusion and the Xception model to enhance the accuracy of early rolling bearing fault diagnosis, and offers a viable solution for health monitoring and fault diagnosis in industrial settings.

Rolling bearing fault diagnosis method based on MTF-MFACNN

A rolling bearing fault diagnosis method based on the Markov transition field (MTF) and multi-scale feature aggregation convolutional neural network (MFACNN) is proposed to address the problems of excessive parameter number, slow training speed, and insufficient generalization of traditional CNNs. Firstly, the original vibration signal is input into the MTF and converted into two-dimensional images with time correlation. Then, in order to effectively aggregate feature information at different scales and levels, a MFA module is presented to capture rich information from feature maps at different scales and assign different weights to these features for fusion. Secondly, while ensuring the lightweight of the model, utilizing feature information of different resolutions, a lightweight feature fusion module is put forward to fuse multiple feature maps together to improve the performance and efficiency of the model. On this basis, an MFACNN model is constructed. Finally, the two-dimensional images are input into MTF-MFACNN and experimentally validated using two different datasets. The results show that the proposed method has faster calculation speed, higher fault recognition accuracy, and stronger generalization performance compared to other methods.

Mechanism Study of Symmetric Mechanism-Rolling Bearing Dynamic System Considering the Influence of Temperature Factor

Due to the symmetry of their structure and the way they rotate, rolling bearings are often used at high temperatures and high speeds. When the temperature changes, the material properties of the rolling bearing change, which in turn causes the dynamic model equation of the rolling bearing to change. Given the problem in that the conventional dynamic model equation does not consider temperature changes, but because temperature changes cause changes in material performance parameters resulting in differences between the dynamic simulation signals and the actual signals, this paper fully considers three factors, namely, the thermal expansion coefficient, the hardness value, and the friction coefficient. With the influence of temperature changes, a study on the mechanism of the symmetric mechanism-rolling bearing dynamic system considering the influence of temperature factors is proposed. First, combined with the material properties of the rolling bearing, the changes in thermal expansion coefficient, hardness value, and friction coefficient caused by temperature changes were analyzed. Second, the functional relationship expressions obtained above were substituted into the existing conventional dynamic model. Finally, the comprehensive functional relationship formula was substituted into the conventional dynamic model to obtain the rolling bearing dynamic model under temperature difference changes. By studying the mechanism of the symmetric mechanism-rolling bearing dynamic system that considers the influence of temperature factors, this method can undertake a more comprehensive consideration of the dynamic analysis research into rolling bearing fault diagnosis, thereby verifying the effectiveness of the method.

Fault diagnosis of high-speed rolling bearing in the whole life cycle based on improved grey wolf optimizer-least squares support vector machines

Under the high-speed condition, the fault diagnosis of rolling bearing is difficult due to parameter limitation and local optimization. To solve these problems, a fault diagnosis method of the whole life cycle based on wavelet thresholding denoising, genetic algorithm-variational mode decomposition (GA-VMD) and improved grey wolf optimizer-least squares support vector machines (IGWO-LSSVM) is proposed. The nonlinear convergence factor is adopted to improve the global search ability of GWO, and then LSSVM is optimized by IGWO, which is used to improve the fault identification accuracy. Based on the high-speed rolling bearing test rig, this method is applied to fault diagnosis of high-speed rolling bearing in the whole life cycle. The kurtosis and approximate entropy indexes are adopted as the basis for the stage division in the degradation process. The fault diagnosis of rolling bearing in the whole life cycle is completed from the five aspects of data acquisition, stage division, data preprocessing, fault feature extraction and fault feature identification. The results show that the maximum identification accuracy of this method can reach 97 %.

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

When using data-driven methods for fault diagnosis of mechanical rotating components such as gears and bearings, there is a problem of class imbalance in the lifecycle data collected by sensors. The most commonly used method to address this issue is the synthetic minority over-sampling technique, which synthesizes samples in the feature space, but its blind synthesis may lead to redundant features in the synthetic samples. To avoid this deficiency, this paper proposes a feature-weighted oversampling method called AFS-O (Attention Features Selection Oversampling Technique). First, time–frequency domain features are extracted from the full lifecycle data of bearings to construct an initial subset of features, which serves the input to AFS. Then, AFS is then used to obtain the distribution of feature selection patterns and generate feature weights to determine the inclusion or exclusion of each feature, thereby constructing an optimal subset of features. Finally, the optimal feature subset is synthetically oversampled to achieve class-balanced data, which is then fed into a classifier. AFS-O is applied to the rolling bearing accelerated lifetime dataset from Xi’an Jiaotong University. Experimental results demonstrate that AFS-O outperforms other state-of-the-art synthetic oversampling algorithms in terms of Gmean, , and Recall, confirming the effectiveness of the proposed method.

Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings

Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings

A novel HB-SC-MCCNN model for intelligent fault diagnosis of rolling bearing

A novel HB-SC-MCCNN model for intelligent fault diagnosis of rolling bearing

Intelligent Fault Diagnosis of Rolling Bearings Based on an Improved Empirical Wavelet Transform and ResNet Under Variable Conditions

Intelligent Fault Diagnosis of Rolling Bearings Based on an Improved Empirical Wavelet Transform and ResNet Under Variable Conditions

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

LW-BPNN: A Novel Feature Extraction Method for Rolling Bearing Fault Diagnosis

Efficiently diagnosing bearing faults is of paramount importance to enhance safety and reduce maintenance costs for rotating machinery. This paper introduces a novel bearing fault diagnosis method (LW-BPNN), which combines the rich properties of Legendre multiwavelet bases with the robust learning capabilities of a BP neural network (BPNN). The proposed method not only addresses the limitations of traditional deep networks, which rely on manual feature extraction and expert experience but also eliminates the complexity associated with designing and training deep network architectures. To be specific, only two statistical parameters, root mean square (RMS) and standard deviation (SD), are calculated on different Legendre multiwavelet decomposition levels to thoroughly represent more salient and comprehensive fault characteristics by using several scale and wavelet bases with various regularities. Then, the mapping relation between the extracted features and the health conditions of the bearing is automatically learned by the simpler BPNN classifier rather than the complex deep network structure. Finally, a few experiments on a popular bearing dataset are implemented to verify the effectiveness and robustness of the presented method. The experimental findings illustrate that the proposed method exhibits a high degree of precision in diagnosing various fault patterns. It outperforms other methods in terms of diagnostic accuracy, making it a viable and promising solution for real-world industrial applications in the field of rotating machinery.

A Siamese CNN-BiLSTM-based method for unbalance few-shot fault diagnosis of rolling bearings

Small and imbalanced fault samples have a profound impact on the diagnostic performance of a model in the process of locating and quantifying the rolling bearing damage of aeroengines in practice. Therefore, a Siamese Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model was proposed in this paper. Random selection and cross combination methods were used to augment and balance sample sizes at first. Then, two weight-sharing CNN-BiLSTM models were used for adaptive extraction and distance measurement of weak fault features. Finally, the fault classification was performed based on feature distance. Model performance was verified using simulated fault test data of rolling bearings. The results showed that the Siamese CNN-BiLSTM model could achieve an accuracy of up to 96.0% for quantitative diagnosis and 98.0% for location diagnosis. This model was also capable of solving the imbalanced classification of samples and made it possible to transfer between different rotating speeds and working conditions.

Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis

The dual-classifier domain adaptation methods concentrate on the output consistency between two classifiers, while ignoring the classification determinacy of each classifier and reducing the fault identification capability of the model. To address this challenge, a self-supervised learning-based dual-classifier domain adaptation model (SLDDA) is presented for cross-domain fault diagnosis of bearings. Firstly, a dual-classifier classification determinacy metric is formulated to alleviate the output ambiguity between classifiers, which simultaneously considers the joint determinacy between two classifiers along with the individual determinacy of each classifier. Secondly, a self-supervised learning approach based on the clustering of target data is proposed to extract abundant target features from the target data. Furthermore, two validation experiments are conducted on bearing datasets of Paderborn University (PU) and H0205. Comparing with the Maximum Classifier Discrepancy method, the proposed SLDDA improves the diagnostic accuracy by an average of 14 % of the PU dataset, which efficiently fulfills the cross-domain fault diagnosis of rolling bearings under variable operating conditions.

A Mathematical Morphological Network Fault Diagnosis Method for Rolling Bearings Based on Acoustic Array Signal

To extract valuable characteristic information from the acoustic radiation signal of rolling bearings, a novel mathematical morphological network (MMNet) is proposed. First, a mathematical morphological network layer is constructed by leveraging the advantages of a multi-scale enhanced top-hat morphological operator (MEAVGH) that can extract positive and negative pulses, which are then integrated into the deep learning network. Second, the input signal undergoes processing with different scale structural elements (SEs) to obtain multi-branch data. This is followed by channel attention and spatial attention mechanism-based weighting of the generated multi-branch data. Finally, the fused information is fed to the neural network to yield the final result. The experimental results demonstrate the efficacy of the proposed method in extracting fault feature information, achieving a fault classification accuracy of 98.56%. Furthermore, the algorithm exhibits robustness and high training efficiency. Comparative analysis reveals that the proposed method outperforms other approaches regarding cluster analysis, accuracy, recall rate, and computational efficiency. These findings further highlight the advantages of MMNet in acoustic signal-based fault diagnosis for rolling bearings.

DEC-NWD: an approach for open set domain adaptation in fault diagnosis

The issue of data-driven cross-domain fault diagnosis for rolling bearings has been effectively addressed through advancements in domain adaptation (DA) methods. However, most existing approaches assume the same set of labels for training data and test data. This assumption often falls short of reality, as new fault types may emerge during the testing phase, resulting in less effective DA methods based on marginal distribution. To address this issue, this study proposes an open set DA method based on domain similarity, entropy, confidence, and nuclear-norm 1-Wasserstein discrepancy (NWD). Within this method, a sample-level transferability criterion is introduced. This criterion quantifies the transferability of target samples and assigns small weights to the unknown class. The complementary nature of entropy and confidence is exploited to improve the discriminability of the network for highly uncertain predictions and to use multiple classifiers to compensate for the possible influence of prediction errors on confidence. Additionally, the NWD is utilized in this method. It treats the classifier as a discriminator and leverages the predicted discriminative information to maximize the alignment of the common classes between the source and target domains. The proposed method has been validated through extensive experiments conducted on two publicly available bearing datasets.

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

Bearings are one of the critical components of any mechanical equipment. They induce most equipment faults, and their health status directly impacts the overall performance of equipment. Therefore, effective bearing fault diagnosis is essential, as it helps maintain the equipment stability, increasing economic benefits through timely maintenance. Currently, most studies focus on extracting fault features, with limited attention to establishing fault thresholds. As a result, these thresholds are challenging to utilize in the automatic monitoring diagnosis of intelligent devices. This study employed the generalized fractal dimensions to effectively extract the feature of time-domain vibration signals of bearings. The optimal fault threshold model was developed using the receiver operating characteristic curve, which served as the baseline of exception judgment. The extracted fault threshold model was verified using two bearing operation experiments. The experimental results revealed different damaged positions and components observed in the two experiments. The same fault threshold model was obtained using the method proposed in this study, and it effectively diagnosed the abnormal states within the signals. This finding confirms the effectiveness of the diagnostic method proposed in this study.

Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified Sparse Bayesian Classification Model.

Fault diagnosis of rotating machinery plays an important role in modern industrial machines. In this paper, a modified sparse Bayesian classification model (i.e., Standard_SBC) is utilized to construct the fault diagnosis system of rotating machinery. The features are extracted and adopted as the input of the SBC-based fault diagnosis system, and the kernel neighborhood preserving embedding (KNPE) is proposed to fuse the features. The effectiveness of the fault diagnosis system of rotating machinery based on KNPE and Standard_SBC is validated by utilizing two case studies: rolling bearing fault diagnosis and rotating shaft fault diagnosis. Experimental results show that base on the proposed KNPE, the feature fusion method shows superior performance. The accuracy of case1 and case2 is improved from 93.96% to 99.92% and 98.67% to 99.64%, respectively. To further prove the superiority of the KNPE feature fusion method, the kernel principal component analysis (KPCA) and relevance vector machine (RVM) are utilized, respectively. This study lays the foundation for the feature fusion and fault diagnosis of rotating machinery.

A maximum cyclic impulses energy ratio deconvolution fused with enhanced envelope derivative operator frequency spectrum for early fault diagnosis of rolling bearing

Blind deconvolution is a powerful tool for rolling bearing fault diagnosis. As one of deconvolution methods, maximum second-order cyclostationarity blind deconvolution (CYCBD) is proved to be effective in extracting bearing fault characteristics. However, the performance of CYCBD method is greatly compromised by setting of fault characteristic frequency (FCF) in advance. Moreover, its performance decreases dramatically under the interference of random shocks and strong noise. To address these issues, a new deconvolution method, named as maximum cyclic impulses energy ratio deconvolution (MCIERD) fused with enhanced envelope derivative operator frequency spectrum (EEDOFS) is proposed in this research. In this method, the EEDOFS is proposed to estimate the FCF. Furthermore, the cyclic impulses energy ratio (CIER) is employed as the deconvolution indicator. Moreover, the hybrid firefly and particle swarm optimization algorithm is used to solve the optimal filter coefficients by maximizing the CIER. Simulation results show that EEDOFS exhibits a greater robustness in estimating FCF accurately under strong interferences and MCIERD performs well in extracting fault cyclic impulses under the interference of heavy noise and random shocks. Finally, three run-to-failure bearing datasets are employed for experimental validation, whose results demonstrate the effectiveness of EEDOFS in accurate estimating FCF and identifying the early bearing fault. Meanwhile, MCIERD fused with EEDOFS is proved to have greater advantages in extracting early bearing fault feature.

A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis

Bearing fault is a common factor leading to machine failures. How to extract the periodic transient signal due to bearing faults submerged in strong noise is a challenging problem for bearing fault diagnosis. Total variation denoising is a method used to remove noise and extract features. However, it solely relies on the sparsity of the first-order difference of the signal, resulting in the loss of important features and underestimation of amplitude. Additionally, it fails to capture the periodicity of the signal. To address these issues, a novel approach called fusion non-convex group sparsity difference (FNC-GSD) method is proposed for bearing fault diagnosis. Firstly, it is recognized that noise does not exhibit sparsity in the difference domain, while transient signal exhibits group sparsity in the difference domain. This grouping property enhances the feature selection ability of sparse model. Inspired by this, the group sparsity of the transient signal in the difference domain is used to preserve the fault features as much as possible. Additionally, in order to promote sparsity of the signal itself in the time domain to preserve the potential impulse component, a l 1-norm regularization term is introduced. Furthermore, a non-convex sparsity-inducing penalty function strategy is employed to prevent amplitude underestimation. The proposed sparse model considers both the group sparsity in the difference domain and the sparsity in the time domain of the transient signal, and its solution is derived according to the majorization–minimization algorithm. And the particle swarm optimization algorithm is used to adaptively search the regularization parameters of FNC-GSD. Finally, multiple bearing fault diagnosis experiments are conducted to demonstrate the performance of the FNC-GSD. The results show that it has advantages in fault feature extraction compared with some other methods.

A rolling bearing fault diagnosis method based on a convolutional neural network with frequency attention mechanism

A convolutional neural network fault diagnosis method based on frequency attention mechanism was designed for the problem that the traditional method cannot adaptively extract effective feature information in rolling bearing fault diagnosis and the diagnosis effect of rolling bearing is poor under strong environmental noise interference. Firs, the Mel-frequency cepstral coefficient (MFCC) of the bearing vibration signal was extracted. Second, to solve the problem of the channel attention mechanism adopting global average pooling (GAP) and neglecting channel internal characteristic information, the GAP was extended in the frequency domain, and a two-stage frequency component selection criterion was designed. The results show that the MFCC method can extract fault-sensitive features in industrial noise environments, improve the existing channel attention mechanism using frequency domain attention mechanism, and overcome the information loss caused by GAP of convolutional layer features in channel attention mechanism. Identification accuracy, recall rate, and F1-score are 100% on the rolling bearing simulation fault datasets of Case Western Reserve University and Central South University. Compared with the convolutional block attention module, the accuracy of the method combining spatial attention mechanism and channel attention mechanism is improved by 0.34 and 0.24%, respectively, and compared with other front-bearing fault diagnosis methods, it also offers significant improvement.

Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis

In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search algorithm (LSSA) based on backward learning of lens imaging and Gaussian Cauchy variation is proposed. The lens imaging reverse learning strategy enhances the traversal capability of the algorithm and allows for a better balance of algorithm exploration and development. Then, the performance of the proposed LSSA was tested on the benchmark function. Finally, LSSA is used to find the optimal modal component K and the optimal penalty factor α in VMD-GRU, which in turn realizes the fault diagnosis of rolling bearings. The experimental results show that the model can achieve a 96.61% accuracy in rolling bearing fault diagnosis, which proves the effectiveness of the method.