Bearing Fault Research Articles

A Novel Collaborative Bearing Fault Diagnosis Method Based on Multisignal Decision-Level Dynamically Enhanced Fusion

A Novel Collaborative Bearing Fault Diagnosis Method Based on Multisignal Decision-Level Dynamically Enhanced Fusion

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy

Convolutional Neural Network Design Based on Weak Magnetic Signals and Its Application in Aircraft Bearing Fault Diagnosis

Convolutional Neural Network Design Based on Weak Magnetic Signals and Its Application in Aircraft Bearing Fault Diagnosis

Time-Shift Denoising Combined With DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current Signals

Time-Shift Denoising Combined With DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current Signals

Electric Motor Bearing Fault Noise Detection via Mel-Spectrum-Based Contrastive Self-Supervised Transformer Model

Electric Motor Bearing Fault Noise Detection via Mel-Spectrum-Based Contrastive Self-Supervised Transformer Model

A Gramian Angular Field for Constructing Graph-Based GNNs and Its Applications in Rolling Bearing Defect Detection

A Gramian Angular Field for Constructing Graph-Based GNNs and Its Applications in Rolling Bearing Defect Detection

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

The unsupervised bearing fault diagnosis method based on the dual-framework Siamese network

Abstract Aiming at the problem of insufficient bearing fault samples in practical engineering, an unsupervised bearing fault diagnosis method with double frame twin network is proposed in this paper. This method only uses the source domain data to diagnose the target domain data, and can minimize the problem of domain difference. The concrete method is firstly to carry on continuous wavelet transform to the rolling bearing signal, and the processed signal is used as the input feature of convolutional neural network. The twin network structure is optimized and Manhattan distance is used as the loss function in the training process. The proposed CM network is applied to the diagnosis of inner ring fault, outer ring fault and normal state of rolling bearing. The results show that CM network can achieve over 90% accuracy on SQ variable speed vibration signal data set (SQV), Shijiazhuang Railway University data set (STD) and Case Western Reserve University data set (CWRU), which is suitable for bearing fault diagnosis under different working conditions and provides a new direction for fault diagnosis. The PyTorch code is available at https://github.com/xiaotianqu/The-unsupervised-bearing-fault-diagnosis-method-based-on-the-dual-framework-Siamese-network.

Localized Bearing Fault Analysis for Different Induction Machine Start-Up Modes via Vibration Time-Frequency Envelope Spectrum.

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time-frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes.

Dynamic mode decomposition-based technique for cross-term suppression in the Wigner-Ville distribution

This paper presents a new method for time-frequency representation (TFR) using dynamic mode decomposition (DMD) and Wigner-Ville distribution (WVD), which is termed as DMD-WVD. The proposed method helps in removing cross-term in WVD-based TFR. In the suggested method, the DMD decomposes the multi-component signal into a set of modes where each mode is considered as mono-component signal. The analytic modes of these obtained mono-component signals are computed using the Hilbert transform. The WVD is computed for each analytic mode and added together to obtain cross-term free TFR based on the WVD. The effectiveness of the proposed method for TFR is evaluated using Rényi entropy (RE). Experimental results for synthetic signals namely, multi-component amplitude modulated signal, multi-component linear frequency modulated (LFM) signal, multi-component nonlinear frequency modulated (NLFM) signal, multi-component signal consisting of LFM and NLFM mono-component signal, multi-component signal consisting of sinusoidal and quadratic frequency modulated mono-component signals, and synthetic mechanical bearing fault signal and natural signals namely, electroencephalogram (EEG) and bat echolocation signals are presented in order to show the effectiveness of the proposed method for TFR. It is clear from the results that the proposed method suppresses cross-term effectively as compared to the other existing methods namely, smoothed pseudo WVD (SPWVD), empirical mode decomposition (EMD)-WVD, EMD-SPWVD, variational mode decomposition (VMD)-WVD, VMD-SPWVD, and DMD-SPWVD.

Bearing defect detection based on the improved YOLOv5 algorithm.

In the field of bearing defect detection, Aiming at the problem of low efficiency in manual inspection and prone to missed detections in scenarios with small target defects, and overlapping targets, an improved YOLOv5-based object detection method is proposed. Firstly, in terms of feature extraction, the C3 modules in the original backbone of YOLOv5 are replaced with the finer-grained Res2Block modules to improve the model's feature extraction ability. Secondly, in terms of feature fusion, a Bidirectional Feature Pyramid Network (BiFPN) is added to the original neck of YOLOv5 to enhance the fusion ability of shallow graphic features and deep semantic features. Finally, the performance of the improved YOLOv5 algorithm is validated through ablation experiments and comparative experiments with other defect detection algorithms, including the Small_obj algorithm the existing method of adding a small target detection head for identifying small target defects. The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.

Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference

Bearing health is key for maintaining good performance and safety in rotating machinery. As the diagnosis of mechanical faults develops toward intelligence and automation, accurate and systematic fault diagnosis algorithms are imperative. Focusing on the diagnosis of rolling bearing failures, this study utilizes a sliding time window to extract essential data segments. A series of signal processing techniques, including filtering, amplitude–frequency analysis, Hilbert envelope analysis, and energy analysis, is applied to establish a comprehensive dataset. For extraction of the hidden properties of the data, the recurrence quantity spectrum is defined for the input of the neural network. The goal is to obtain a cleaner dataset with enhanced features. A convolution neural network is constructed. Different activation functions in the activation layer are compared for better fault diagnosis algorithms. The established feature matrices are specifically defined to accurately identify the subtlest defects of bearings, thereby facilitating early detection. The proposed procedure distinguishes various fault modes. As for the multidimensional complexities of fault signals, this study carries out a comprehensive comparison of energies, recurrence quantification, and amplitude–frequency characteristics of bearing fault detection to assess the accuracy, computational efficiency, and robustness of bearing fault diagnosis. The proposed method and bearing fault detection procedures have potential in practical applications.

Dynamical analysis and fault detection application of a time-delayed multi-stable stochastic resonance system driven by white correlated noises.

Bearing fault diagnosis is a crucial means to ensure the normal operation of machinery, reduce maintenance costs, enhance equipments safety and extend the service life of bearings. However, the noise interference in input signals often affects the effective extraction of bearing fault signals. In this paper, a time-delay multi-stable stochastic resonance (SR) system driven by white correlated noises is proposed. Firstly, the expressions of the steady-state probability density (SPD) function and the mean first passage time (MFPT) is derived. Simultaneously, we delve into the influences of various system parameters, including multiplicative noise intensity, additive noise intensity, noise correlated strength, time-delay, and time-delay feedback intensity, on the dynamical behavior exhibited by particles within the system. Then, according to the signal-to-noise ratio (SNR) formula, the paper investigates the impact of system parameters on the SNR. It is found that the ease of SR occurrence is directly related to the system parameters. Finally, the experimental results demonstrate that the proposed method exhibits superior performance in detecting faults compared with the classical bistable SR system and the quad-stable SR system.

Dynamic Modeling of Angular Contact Ball Bearing with Local Defects under EHL Considering Impact Force

In order to analyze the operating status of angular contact ball bearings(ACBBs) with local defects in more detail, a dynamic model of ACBB with local defects considering impact force under elastohydrodynamic lubrication conditions is proposed. Firstly, an elastohydrodynamic lubrication model for defective bearings considering spin is established, the film thickness and film stiffness of the defective bearings are calculated, and then time-varying displacement excitation model and time-varying stiffness model for local defects in angular contact ball bearings is presented. Based on this, an instantaneous impact force function related to defect size and bearing speed is established. Secondly, based on Hertz contact theory and impact force function, a dynamic calculation method for ACBBs with local defects on the outer ring is proposed. Finally, the vibration characteristics of ball bearings with faults are investigated, and the influence of different parameters on the dynamic response of the bearings is analyzed. The calculation results show that under lubrication conditions, the impact force on the vibration of the bearing is significantly weakened. With the increase of defect size and load, the frequency of bearing fault characteristics remain unchanged, but its amplitude increases. As the bearing speed increases, the characteristic frequency and amplitude of bearing faults increase.

Time-varying displacement excitation and dynamic modeling of angular contact ball bearing with local defects

Angular contact ball bearings will produce fault damage when working for a long time, thus affecting the normal operation of the system. This paper takes angular contact ball bearings with local defects on the outer ring as the research object, proposes a method to distinguish different local defect profiles, establishes a generalized characterization model of time-varying displacement excitation of local defects in angular contact ball bearings, and studies the evolution process of local defects and their displacement excitation mechanism. On this basis, considering the influence of time-varying displacement caused by bearing defects on dynamic characteristics, based on Hertz contact theory, a fault dynamics model of angular contact ball bearings is established, and the correctness of the established model is verified by experiments. In addition, the analysis results show that rectangular local defects will eventually evolve into trapezoidal shapes; the displacement excitation change trends induced by different defect morphologies are different; compared with the length of local defects, the width has a greater impact on displacement excitation. The research results provide a theoretical basis for bearing optimization design and fault diagnosis.

The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis

Abstract The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem regarding how to 1) accurately select the frequency bands containing rich fault feature information, 2) effectively extract damage information from different frequency bands, and thereby, 3) successfully achieve fault diagnosis for machinery condition monitoring. To address these issues, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra of the selected components, thus obtaining the reconstructed squared envelope spectra for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.

Train Bearing Fault Diagnosis Based on Time–Frequency Signal Contrastive Domain Share CNN

Train Bearing Fault Diagnosis Based on Time–Frequency Signal Contrastive Domain Share CNN

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

An Artificial Neural Network with Bayes' Theorem (Hybrid) for Automated Bearing Faults Diagnosis

An Artificial Neural Network with Bayes' Theorem (Hybrid) for Automated Bearing Faults Diagnosis

Utilizing Bayesian generalization network for reliable fault diagnosis of machinery with limited data

To address the issues of overfitting, domain generalization challenges, and lack of credibility brought by limited data samples in mechanical fault diagnosis in practical engineering, this paper proposes a reliable Bayesian generalization network (BGNet). A Bayesian convolutional layer is constructed based on variational inference, treating all parameters in the convolutional layer as random variables. This approach makes a single model function similar to an ensemble of an infinite number of models, and thus enhancing the model's capability of overfitting resistance and domain generalization. The parameters of the variational distribution are updated to approximate the posterior distribution by local reparametrization and Monte Carlo sampling to optimize the evidence lower bound (ELBO) loss. Confidence information is extracted from the model results and, uncertainty estimation and decomposition schemes are designed to provide interpretability. The proposed method is applied to analyze the experimental data of bearing and gearbox faults. The results show that in a multi-source domain scenario with limited samples, the proposed method demonstrates high diagnostic accuracy, effectively describes the relationship between domain variability and uncertainty, and significantly outperforms several benchmark and state-of-the-art models.