Rolling Bearings Research Articles

Rolling bearing fault analysis based on variational mode decomposition and multiscale arrangement entropy

Rolling bearings in operation will appear nonlinear characteristics of the fault vibration signal. In the process of fault feature extraction, a single permutation entropy (PE) produces unsatisfactory results and low accuracy. In this paper, a new diagnostic method was proposed, which was based on variational mode decomposition (VMD) and multiscale permutation entropy (MPE) to diagnose and analyze rolling bearing faults, multi-scale aligned entropy features of intrinsic mode function (IMF) of faulty vibration signals were extracted, and then support vector machine (SVM) and K-nearest neighbor algorithm (KNN) were used to analyze these features, and the maximum attribution metrics were used to determine classification results. The test results show that this method can improve the detection accuracy by comparing with other test analysis methods.

Prediction of rotation of a shrink-fitted cup of a tapered roller bearing under transient thermal loading

Modeling of tapered roller bearing, running torque and effect of the environment are often studied separately. However, bearing environment can have a significant impact on the behavior of the assembly and bearing performances. In some tapered roller bearing mountings, it is usual to shrink fit the cup of the bearing into its housing. Consequently, high transient thermal loads can lead to a decrease in clamping when the cup and its housing do not have the same thermal expansion coefficients. Under these conditions, the cup may start to rotate in the housing due to the torque transmitted by the bearing. Rotation of the outer ring could strongly affect the bearing performance. This study aims at predicting the risk of cup rotation.

Degradation prediction for mechanical components based on transfer learning and a multistage statistical model

Rolling bearings serve as vital elements in mechanical components; their degradation significantly influences equipment reliability and safety. Previous studies primarily focus on single-stage degradation methods, limiting their applicability for predicting degradation trends across varying conditions and hindering generalization. To address this challenge, we propose a multistage statistical model and a multistage transfer learning based on bidirectional gated recurrent unit with an attention mechanism (MSTL-BGRUA) to predict the multistage degradation trend of bearings under cross-working conditions. Given the nonlinear properties of bearing degradation, we present an online multistage division method to identify degradation characteristics. The degradation features of bearing’s vibration signal are extracted and classified into multiple degradation stages based on a mutation identification policy via the statistical model. Subsequently, a BGRUA model is proposed to balance forward and backward information and assign different weights, achieving accurate prediction for each identified degradation stage. Based on the presented BGRUA, we incorporate an MSTL method for degradation prediction to maintian accuracy under crossing working conditions. Finally, a prognosis framework using both the statistical model and the MSTL-BRGUA model is presented to predict bearing status under different degradation stages. A validation of the presented method is conducted using a vibration dataset, demonstrating its superior predictive performance compared to other advanced degradation prediction methods.

Fault diagnosis method for rolling bearings based on BICNN under complex operating conditions

Fault diagnosis method for rolling bearings based on BICNN under complex operating conditions

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Optimizing Multi-Row Cam Roller Bearing for Long Fatigue Life of Hydraulic Motors

Cam-lobe radial-piston hydraulic motors are widely used as rotation driving units for various marine machinery owing to their ultrahigh output torque (more than 100 kN m). A multi-row cam roller bearing (MCRB) is the key component that directly determines the fatigue life of a cam-lobe radial-piston hydraulic motor. However, compact geometry and complex loads render MCRB susceptible to fatigue failure, highlighting the need for an optimized MCRB to achieve longer fatigue life and higher reliability. Therefore, this study proposes an innovative geometry optimization method for an MCRB to improve its fatigue life. In this method, a quasi-static model was developed to calculate the load distribution, with the fatigue life of the MCRB calculated using both basic dynamic loading and load distribution. Subsequently, a genetic algorithm was used to obtain the optimized geometry parameters, which significantly improved the fatigue life of the MCRB. Finally, a loading test was conducted on a hydraulic motor installed with both the initial and optimized MCRB to validate the effectiveness of the proposed optimization method. This study provides a theoretical guideline for optimizing the design of MCRB, thereby increasing the fatigue life of hydraulic motors.

Fault diagnosis of rolling bearing based on adaptive attention network and federated learning

Abstract For rolling bearings in actual industrial scenarios, the problem of low diagnostic accuracy is caused by the large difference in data distribution under different working conditions and the complexity of working conditions with a lot of redundant information, in this paper, a combination of the Federated Adaptive Attention Network (FAAN) based fault diagnostic model is proposed. Firstly, the fault information is randomly divided into multiple sequences, by utilizing dual convolutional layers and adaptive attention mechanism to process abundant vibration data, it enables accurate identification of fault information distribution in the original signal while removing superfluous information nd fusing characteristics to improve diagnostic accuracy. Furthermore, a federated learning model incorporating attention mechanism is proposed. It performs asynchronous updates based on local data distribution, improving the efficiency and accuracy of data analysis uploads, and enhancing the model’s generalization capability. Simulation experiments have been carried out using the datasets from Case Western Reserve University and Jiangnan University, and after comparative analysis, the proposed method has better performance and generalization ability.

A multi-fault diagnosis method for rolling bearings

A multi-fault diagnosis method for rolling bearings

A core space gradient projection-based continual learning framework for remaining useful life prediction of machinery under variable operating conditions

Recently, continual learning has received particular attention in machinery remaining useful life (RUL) prediction, which enables prognostics networks to gradually improve performance without laborious retraining. Existing studies, however, have the following limitations: 1) The impacts of variable operating conditions are not explicitly considered during continual learning. 2) The continual learnability of prognostics network is limited by the lack of knowledge compression and de-redundancy. To overcome the abovementioned limitations, a novel continual learning framework is proposed for RUL prediction of machinery under variable operating conditions. First, a multi-kernel swarm convolution block is devised to automatically capture degradation features during continual learning without increasing computational consumption, which combines convolutional operations at various scales and focuses attention on appropriate scales based on operating condition information. Then, core space gradient projection is proposed for continual learning of prognostics networks, which mitigates forgetting by guiding gradient descent along the orthogonal direction of the previously input subspace. This approach also ensures the network learnability during continual learning by knowledge compression and de-redundancy to identify core space. The proposed framework is verified using accelerated degradation datasets of rolling element bearings with variable operating conditions. Experimental results show that the proposed framework is superior to some existing continual learning-based prognostics approaches for RUL prediction under variable operating conditions.

Fault Diagnosis Method of Special Vehicle Bearing Based on Multi-Scale Feature Fusion and Transfer Adversarial Learning.

To address the issues of inadequate feature extraction for rolling bearings, inaccurate fault diagnosis, and overfitting in complex operating conditions, this paper proposes a rolling bearing diagnosis method based on multi-scale feature fusion and transfer adversarial learning. Firstly, a multi-scale convolutional fusion layer is designed to effectively extract fault features from the original vibration signals at multiple time scales. Through a feature encoding fusion module based on the multi-head attention mechanism, feature fusion extraction is performed, which can model long-distance contextual information and significantly improve diagnostic accuracy and anti-noise capability. Secondly, based on the domain adaptation (DA) cross-domain feature adversarial learning strategy of transfer learning methods, the extraction of optimal domain-invariant features is achieved by reducing the gap in data distribution between the target domain and the source domain, addressing the call for research on fault diagnosis across operating conditions, equipment, and virtual-real migrations. Finally, experiments were conducted to verify and optimize the effectiveness of the feature extraction and fusion network. A public bearing dataset was used as the source domain data, and special vehicle bearing data were selected as the target domain data for comparative experiments on the effect of network transfer learning. The experimental results demonstrate that the proposed method exhibits an exceptional performance in cross-domain and variable load environments. In multiple bearing cross-domain transfer learning tasks, the method achieves an average migration fault diagnosis accuracy rate of up to 98.65%. When compared with existing methods, the proposed method significantly enhances the ability of data feature extraction, thereby achieving a more robust diagnostic performance.

Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution

Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value.

Failure Analysis of Pulverized Coal Injection (PCI) Mill Grinding Roller Bearing at Blast Furnace

Failure Analysis of Pulverized Coal Injection (PCI) Mill Grinding Roller Bearing at Blast Furnace

A Review of Machine Tool Motorized Spindle Variable Pressure Preload Technology Research

High-precision spindles are a vital study topic because of the growing demand for highspeed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent.

Nondestructive Detection Algorithm for Si3N4 Bearing Roller Microcrack Characterization Based on Multiscale Gamma Correction and Growth Region Segmentation

Nondestructive Detection Algorithm for Si3N4 Bearing Roller Microcrack Characterization Based on Multiscale Gamma Correction and Growth Region Segmentation

Adaptive spectrum amplitude modulation method for rolling bearing fault frequency determination

Abstract Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to ASAM. Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods.

Prediction of remaining useful life for rolling bearing based on ISOMAP and multi-head self-attention with gated recurrent unit

The remaining useful life of rolling element bearing affects the reliability and stability of the machine. Accurately predicting the remaining useful life of rolling element bearings is always necessary to make maintenance decisions in practical engineering. However, the remaining useful life prediction methods using traditional machine learning are incompetent for the task of training without labels, which consumes computational cost, financial resources, and labor. Therefore, a gated recurrent unit model based on feature dimensionality reduction and the multi-head self-attention mechanism (MHGRU) is established, and a homology algorithm is proposed to train the model and make the prediction. The isometric feature mapping algorithm and a homology algorithm are used in the model training, which incorporates the advantages of appreciable computational efficiency and preservation of automatic labeling for training in engineering. First, 24 basic features are extracted from the life-cycle vibration signals of rolling element bearings to reconstruct fusion features by the isometric feature mapping algorithm, which aims to reduce the feature dimension and improve computational efficiency. Since the multi-head self-attention mechanism in the MHGRU has the ability to comprehensively highlight the attention coefficient for long-term fusion features at different moments, it gives the gated recurrent unit considerable performance in reducing the computational complexity of extremely long time series and improving the accuracy of prediction results. In addition, two open-source IEEE PHM Challenge and XJTU-SY bearing datasets sampled by the sensor are adopted to assess the prediction performance of the MHGRU with homology algorithm. Finally, some existing remaining useful life prediction approaches of rolling element bearings are used for comparison. The experimental results show that MHGRU is suitable for the remaining useful life prediction of rolling element bearings and is superior to other models.

Fault Diagnosis of Rolling Bearings in Agricultural Machines Using SVD-EDS-GST and ResViT

In the complex and harsh environment of agriculture, rolling bearings, as the key transmission components in agricultural machinery, are very prone to failure, so research on the intelligent fault diagnosis of agricultural machinery components is critical. Therefore, this paper proposes a new method based on SVD-EDS-GST and ResNet-Vision Transformer (ResViT) for the fault diagnosis of rolling bearings in agricultural machines. Firstly, an experimental platform for rolling bearing failure in agricultural machinery is built, and one-dimensional vibration signals are obtained using acceleration sensors. Next, the signal is preprocessed for noise reduction using singular value decomposition (SVD) combined with the energy difference spectrum (EDS) to solve for the interference of complex noise and redundant components in the vibration signal. Secondly, generalized S-transform (GST) is used to process vibration signals into images. Then, the ResViT model is proposed, where the ResNet34 network is used to replace the image chunking mechanism in the original Vision Transformer model for feature extraction. Finally, an improved Vision Transformer (ViT) is utilized to synthesize global and local information for fault classification. The experimental results show that the proposed method’s average accuracy in rolling bearing fault classification for agricultural machinery reaches 99.08%. In addition, compared with SVD-EDS-GST-CNN, SVD-EDS-GST-LSTM, STFT-ViT, GST-ViT, and SVD-EDS-GST-ViT, the accuracy rate was improved by 3.5%, 3.84%, 4.8%, 8.02%, and 0.56%, and the standard deviation was also minimized.

A hybrid reliability assessment method based on health index construction and reliability modeling for rolling bearing

AbstractThe assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings’ reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method.

An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

ABSTRACT Remaining Useful Life (RUL) prediction of rolling bearings is one of the intricate and important issues for equipment intelligent maintenance and health management. Various machine learning models and methods have been applied to rolling bearing RUL prediction. However, a single model cannot effectively extract state information and obtain accurate prediction results, and its generalisation is not stable under the condition of small sample data. Therefore this paper proposes an intelligent hybrid deep learning model for achieving accurate RUL prediction of rolling bearings. Firstly, the one-dimensional vibration signal is transformed into the corresponding two-dimensional time-frequency diagram via Continuous Wavelet Transform (CWT). Secondly, the diagram is input into a Multilayer Perceptron (MLP) consisting of a basic three-layer feed-forward network to obtain a one-dimensional feature vector. And lastly, the obtained feature vector is input into an integrated model based on Deep Autoregressive and Transformer to produce the probability distribution and obtain the prediction results of rolling bearing RUL. Extensive experiments on two rolling bearing datasets show that the proposed model outperforms six other comparative models in extracting bearing fault features and predicting bearing RUL, which demonstrates that the proposed model can effectively extract bearing fault features and accurately predict bearing remaining useful life.