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Keywords = rolling-element bearing

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22 pages, 4464 KB  
Article
Fatigue Life Prediction of Main Bearings in Wind Turbines Under Random Wind Speeds
by Likun Fan, Ziwen Wu, Yiping Yuan, Xiaojun Liu and Wenlei Sun
Machines 2025, 13(10), 907; https://doi.org/10.3390/machines13100907 - 2 Oct 2025
Abstract
To address the complex operating conditions and challenging dynamic characteristics of bearings in the main shaft transmission system of wind turbines, this study investigates a specific wind turbine model. By comprehensively considering factors such as main shaft structure, cumulative damage, and stochastic wind [...] Read more.
To address the complex operating conditions and challenging dynamic characteristics of bearings in the main shaft transmission system of wind turbines, this study investigates a specific wind turbine model. By comprehensively considering factors such as main shaft structure, cumulative damage, and stochastic wind loads, we adopt a modular analysis framework integrating the wind field, aerodynamics, the structural response, and fatigue life prediction to establish a method for predicting the fatigue life of main shaft bearings under stochastic wind conditions. To verify this method, the fixed-end main shaft bearing of a 4.5 MW wind turbine was selected as a case study. The research results show the following: (1) Increases in both average wind speed and turbulence intensity significantly shorten the fatigue life of the bearing. (2) Higher turbulence intensity amplifies the dispersion of the speed and load of rolling elements, thereby increasing the probability of extreme operating conditions and exerting an adverse impact on fatigue life. (3) The average wind speed has a significant influence on the overall fatigue life: within a specific range, the fatigue failure probability of the main bearing increases as the average wind speed decreases. (4) The impact of wind speed fluctuations on the hub center load is much greater than that caused by rotational speed changes. (5) In addition, the modular design method adopted in this study calculates that the fatigue life of the fixed-end bearing is 28.8 years, with an overall error of only 0.8 years compared to the 29.6-year fatigue life obtained using Romax simulation software. This research provides important theoretical references and engineering value for improving the operational reliability of wind turbines and enhancing the accuracy of bearing fatigue life prediction. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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15 pages, 5285 KB  
Article
A Multi-Layer Triboelectric Material Deep Groove Ball Bearing Triboelectric Nanogenerator: Speed and Skidding Monitoring
by Zibao Zhou, Long Wang, Zihao Wang and Fengtao Wang
Machines 2025, 13(9), 875; https://doi.org/10.3390/machines13090875 - 19 Sep 2025
Viewed by 312
Abstract
With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip [...] Read more.
With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip state of the rolling elements. The DGTG utilizes a copper inner ring charge supplementation mechanism to maintain the maximum charge density on the rolling element, thereby ensuring a strong electrical signal output. The deviation between the output frequency of the electrical signal and the theoretical value allows for effective monitoring of the slip state during bearing operation. Experimental results demonstrate that when the inner ring speed ranges from 100 to 2000 rpm, the open-circuit voltage generally remains above 30 V. The short-circuit current signal exhibits a fitting coefficient of R2 = 0.99997 with respect to the roller’s rotational speed frequency and motor speed, while the open-circuit voltage signal shows a fitting coefficient of R2 = 0.99984, indicating a strong linear relationship and a good response to varying speeds. Compared to the traditional photoelectric sensors commonly used in industry, the measurement difference between the three signals is consistently less than 5.5%, and real-time monitoring of the slip rate is possible when compared to the theoretical value. The DGTG developed in this study occupies minimal space, offers high reliability, and fully leverages the bearing structure, enabling real-time monitoring of bearing speed and slip. Full article
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24 pages, 3033 KB  
Article
Research on Fault-Diagnosis Technology of Rare-Earth Permanent Magnet Motor Based on Digital Twin
by Yangrui Ma and Yaqiao Zhu
Symmetry 2025, 17(9), 1494; https://doi.org/10.3390/sym17091494 - 9 Sep 2025
Viewed by 433
Abstract
To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of [...] Read more.
To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of symmetry, whereas faults introduce identifiable and distinct asymmetries. This study constructs a high-fidelity digital twin model based on the five-dimensional model theory to simulate both the symmetrical (healthy) state and various asymmetrical faulty states of motor bearings—specifically, inner race, outer race, and rolling element faults—thereby effectively addressing the critical issue of data scarcity. Building upon this framework, fault features characterizing these asymmetries are accurately extracted using an optimized variational mode decomposition (VMD) algorithm and subsequently classified with a convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model. The results validate the model’s ability to accurately replicate bearing-fault data. The proposed diagnostic method achieves a stable and high average accuracy of 98.44 ± 0.41% over multiple runs on the simulation data. Furthermore, its effectiveness was validated on a public real-world bearing dataset, where it achieved an accuracy of over 95%, demonstrating its robustness and potential for industrial applications by effectively identifying fault-induced asymmetries. Full article
(This article belongs to the Section Engineering and Materials)
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25 pages, 4353 KB  
Article
Adaptive Gradient Loading Mechanism of Ball–Column Composite Bearings Considering Collar Deformation
by Guanjie Li, Yongcun Cui, Hedong Wei, Zhiwen Yang and Yanguang Ni
Machines 2025, 13(9), 785; https://doi.org/10.3390/machines13090785 - 1 Sep 2025
Viewed by 418
Abstract
To address the issue of uneven load and premature failure in ball–column composite bearings caused by ring deformation, this study develops a mechanical analysis model, considering ring deformation based on flexible ring theory and rolling bearing design. It systematically examines radial deflection of [...] Read more.
To address the issue of uneven load and premature failure in ball–column composite bearings caused by ring deformation, this study develops a mechanical analysis model, considering ring deformation based on flexible ring theory and rolling bearing design. It systematically examines radial deflection of the ring and how key parameters affect load distribution and stress. The results demonstrate that the elastic deformation of the collar redistributes the load, reduces the roller column’s load-carrying efficiency, and disrupts the optimal load distribution mode. Increasing the number of loaded rolling elements significantly improves the load uniformity, reduces the peak contact stress, and enhances the overall load-carrying performance. By optimizing the clearance matching across three bearings rows, a load-adaptive gradient bearing mechanism is realized by dynamically transferring, 70–90% of the heavy-load optimal distribution. These findings address the domestic research gaps and offer theoretical support for the performance prediction and optimal design of integrated ball–column composite bearings. Full article
(This article belongs to the Section Machine Design and Theory)
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43 pages, 10716 KB  
Article
Fault Diagnosis of Rolling Bearing Acoustic Signal Under Strong Noise Based on WAA-FMD and LGAF-Swin Transformer
by Hengdi Wang, Haokui Wang, Jizhan Xie and Zikui Ma
Processes 2025, 13(9), 2742; https://doi.org/10.3390/pr13092742 - 27 Aug 2025
Viewed by 545
Abstract
To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted [...] Read more.
To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications. Full article
(This article belongs to the Section Process Control and Monitoring)
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22 pages, 9340 KB  
Article
The Effect of Defect Size and Location in Roller Bearing Fault Detection: Experimental Insights for Vibration-Based Diagnosis
by Haobin Wen, Khalid Almutairi, Jyoti K. Sinha and Long Zhang
Sensors 2025, 25(16), 4917; https://doi.org/10.3390/s25164917 - 9 Aug 2025
Viewed by 456
Abstract
In rotating machines, any faults in anti-friction bearings occurring during operation can lead to failures that are unacceptable due to considerable downtime losses and maintenance costs. Hence, early fault detection is essential, and different vibration-based methods (VBMs) are explored to recognise incipient fault [...] Read more.
In rotating machines, any faults in anti-friction bearings occurring during operation can lead to failures that are unacceptable due to considerable downtime losses and maintenance costs. Hence, early fault detection is essential, and different vibration-based methods (VBMs) are explored to recognise incipient fault signatures. Based on rotordynamics, if a bearing defect causes metal-to-metal (MtM) impacts during shaft rotation, the impacts excite high-frequency resonance responses of the bearing assembly. The defect-related frequencies are modulated with the resonance responses and rely on signal demodulation for fault detection. However, the current study highlights that the bearing fault/faults may not be detected if the defect in a bearing is not causing MtM impacts nor exciting the high-frequency resonance of the bearing assembly. In a roller bearing, a localised defect may maintain persistent contact between rolling elements and raceways, thereby preventing the occurrence of impulse vibration responses. Due to contact persistence, such defects may not generate impact and may not be detected by existing VBMs, and the bearing could behave as healthy. This paper investigates such specific cases by exploring the relationship between roller-bearing defect characteristics and their potential to generate impact loads during operation. Using an experimental bearing rig, different roller and inner-race defects are presented while their fault characteristic frequencies remain undetected by the envelope analysis, fast Kurtogram, cyclic spectral coherence, and tensor decomposition methods. This study highlights the significance of both the dimension and location of defects within bearings on their detectability based on the rotordynamics concept. Further, simple roller-beam experiments are carried out to visualise and validate the reliability of the experimental observations made on the roller bearing dynamics. Full article
(This article belongs to the Special Issue Electronics and Sensors for Structure Health Monitoring)
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26 pages, 8019 KB  
Article
Tribo-Dynamic Investigation of Cryogenic Ball Bearings Considering Varying Traction Parameters
by Shijie Zhang, Shuangshuang Jia, Yuhao Zhao, Jing Wei and Yanyang Zi
Lubricants 2025, 13(8), 352; https://doi.org/10.3390/lubricants13080352 - 5 Aug 2025
Viewed by 567
Abstract
The traction behavior in cryogenic solid-lubricated ball bearings (CSLBBs) used in liquid rocket engines (LREs) affects not only the dynamic response of the bearing but also the lubricity and wear characteristics of the solid lubrication coating. The traction coefficient between the ball and [...] Read more.
The traction behavior in cryogenic solid-lubricated ball bearings (CSLBBs) used in liquid rocket engines (LREs) affects not only the dynamic response of the bearing but also the lubricity and wear characteristics of the solid lubrication coating. The traction coefficient between the ball and raceway depends on factors such as contact material, relative sliding velocity, and contact pressure. However, existing traction curve models for CSLBBs typically consider only one or two of these factors, limiting the accuracy and applicability of theoretical predictions. In this study, a novel traction model for CSLBBs is proposed, which incorporates the combined effects of contact material, relative sliding velocity, and contact pressure. Based on this model, a tribo-dynamic framework is developed to investigate the tribological and dynamic behavior of CSLBBs. The model is validated through both theoretical analysis and experimental data. Results show that the inclusion of solid lubricant effects significantly alters the relative sliding and frictional forces between the rolling elements and the raceway. These changes in turn influence the impact dynamics between the rolling elements and the cage, leading to notable variations in the bearing’s vibrational response. The findings may offer valuable insights for the wear resistance and vibration reduction design of CSLBBs. Full article
(This article belongs to the Special Issue Tribological Characteristics of Bearing System, 3rd Edition)
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43 pages, 6462 KB  
Article
An Integrated Mechanical Fault Diagnosis Framework Using Improved GOOSE-VMD, RobustICA, and CYCBD
by Jingzong Yang and Xuefeng Li
Machines 2025, 13(7), 631; https://doi.org/10.3390/machines13070631 - 21 Jul 2025
Cited by 1 | Viewed by 423
Abstract
Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak [...] Read more.
Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article
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27 pages, 9428 KB  
Article
A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
by Hoyeon Lee and Jaehong Yu
Appl. Sci. 2025, 15(14), 7698; https://doi.org/10.3390/app15147698 - 9 Jul 2025
Viewed by 792
Abstract
In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenance activity only when the fault [...] Read more.
In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenance activity only when the fault symptoms are detected. For more accurate fault detection of rotating machinery, vibration signals have been widely used. However, the vibration signals collected from most real rotating machinery are noisy and nonstationary, and signals from fault states also rarely exist. To address these issues, we newly propose a fault detection framework with a spectrogram and convolutional autoencoder. Firstly, the raw vibration signals are transformed into spectrograms to represent both time- and frequency-related information. Then, a two-dimensional convolutional autoencoder is trained using only normal signals. The encoder part of the convolutional autoencoder is used as a feature extractor of the vibration signals in that it summarizes information on the input spectrogram into the smaller latent feature vector. Finally, we construct the fault detection model by applying the one-class classification algorithm to the latent feature vectors of training signals. We conducted an experimental study using vibration signals collected from a rolling element bearing experimental platform. The results confirm the superiority of the proposed fault detection framework on rotating machinery. In the experimental study, the proposed fault detection framework yielded AUROC values of almost one, and this implies that the proposed framework can be sufficiently applied to real-world fault signal detection problems. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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56 pages, 8213 KB  
Article
A Novel Exploration Stage Approach to Improve Crayfish Optimization Algorithm: Solution to Real-World Engineering Design Problems
by Harun Gezici
Biomimetics 2025, 10(6), 411; https://doi.org/10.3390/biomimetics10060411 - 19 Jun 2025
Viewed by 539
Abstract
The Crayfish Optimization Algorithm (COA) has limitations that affect its optimization performance seriously. The competition stage of the COA uses a simplified mathematical model that concentrates on relations of distance between crayfish only. It is deprived of a stochastic variable and is not [...] Read more.
The Crayfish Optimization Algorithm (COA) has limitations that affect its optimization performance seriously. The competition stage of the COA uses a simplified mathematical model that concentrates on relations of distance between crayfish only. It is deprived of a stochastic variable and is not able to generate an applicable balance between exploration and exploitation. Such a case causes the COA to have early convergence, to perform poorly in high-dimensional problems, and to be trapped by local minima. Moreover, the low activation probability of the summer resort stage decreases the exploration ability more and slows down the speed of convergence. In order to compensate these shortcomings, this study proposes an Improved Crayfish Optimization Algorithm (ICOA) that designs the competition stage with three modifications: (1) adaptive step length mechanism inversely proportional to the number of iterations, which enables exploration in early iterations and exploitation in later stages, (2) vector mapping that increases stochastic behavior and improves efficiency in high-dimensional spaces, (3) removing the Xshade parameter in order to abstain from early convergence. The proposed ICOA is compared to 12 recent meta-heuristic algorithms by using the CEC-2014 benchmark set (30 functions, 10 and 30 dimensions), five engineering design problems, and a real-world ROAS optimization case. Wilcoxon Signed-Rank Test, t-test, and Friedman rank indicate the high performance of the ICOA as it solves 24 of the 30 benchmark functions successfully. In engineering applications, the ICOA achieved an optimal weight (1.339965 kg) in cantilever beam design, a maximum load capacity (85,547.81 N) in rolling element bearing design, and the highest performance (144.601) in ROAS optimization. The superior performance of the ICOA compared to the COA is proven by the following quantitative data: 0.0007% weight reduction in cantilevers design (from 1.339974 kg to 1.339965 kg), 0.09% load capacity increase in bearing design (COA: 84,196.96 N, ICOA: 85,498.38 N average), 0.27% performance improvement in ROAS problem (COA: 144.072, ICOA: 144.601), and most importantly, there seems to be an overall performance improvement as the COA has a 4.13 average rank while the ICOA has 1.70 on CEC-2014 benchmark tests. Results indicate that the improved COA enhances exploration and successfully solves challenging problems, demonstrating its effectiveness in various optimization scenarios. Full article
(This article belongs to the Section Biological Optimisation and Management)
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16 pages, 1659 KB  
Article
Influence of Geometric Parameters on Contact Mechanics and Fatigue Life in Logarithmic Spiral Raceway Bearings
by Xiaofeng Zhao, Shuidian Xu, Jinghua Zeng and Tao Xu
Symmetry 2025, 17(6), 889; https://doi.org/10.3390/sym17060889 - 6 Jun 2025
Viewed by 509
Abstract
Symmetrical bearing raceway led to the axial sliding of rolling elements, which is a crucial factor in shortening the operational lifespan. This study addresses this limitation through three-step advancements: first, a parametric equation for logarithmic spiral raceways is developed by analyzing their asymmetric [...] Read more.
Symmetrical bearing raceway led to the axial sliding of rolling elements, which is a crucial factor in shortening the operational lifespan. This study addresses this limitation through three-step advancements: first, a parametric equation for logarithmic spiral raceways is developed by analyzing their asymmetric geometric features; second, based on the geometrical model, we systematically investigate the parameters of the logarithmic spiral that affects the bearing performance metrics; and finally, a novel fatigue life prediction framework that integrates static mechanical analysis with raceway parameters establishes the theoretical foundation for optimizing the raceway parameters. The results of the model analysis show that the error of the maximum contact stress verified by the finite element method is less than 8.3%, which verifies the model’s accuracy. Increasing the contact angle α of the outer ring from 82 to 85 can increase fatigue life by 15.6 times while increasing the initial polar radius O of the inner ring from 7.8 mm to 8.1 mm will cause fatigue life to drop by 86.9%. The orthogonal experiment shows that the contact angle α of the outer ring has the most significant influence on the service life, and the optimal parameter combination (clearance δ of 0.02 mm, inner race and outer race strike angles α of 85°, an inner race initial polar radius ro of 7.8 mm, and an outer race initial polar radius ro of 7.9 mm) achieves a 60.7% fatigue life increase. The findings provide theoretical support and parameter guidance for the optimal bearing design with logarithmic spiral raceways. Full article
(This article belongs to the Section Engineering and Materials)
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22 pages, 14111 KB  
Article
High-Speed Bearing Reliability: Analysis of Tapered Roller Bearing Performance and Cage Fracture Mechanisms
by Qingsong Li, Jiaao Ning, Hang Liang and Muzhen Yang
Metals 2025, 15(6), 592; https://doi.org/10.3390/met15060592 - 26 May 2025
Viewed by 785
Abstract
This investigation examines the fracture mechanisms of 31,311 tapered roller bearing cages using finite element analysis (FEA) and the Gurson–Tvergaard–Needleman (GTN) damage model. Static, dynamic, modal, and harmonic response analyses identify critical stress concentrations at the contact interface between the rolling elements and [...] Read more.
This investigation examines the fracture mechanisms of 31,311 tapered roller bearing cages using finite element analysis (FEA) and the Gurson–Tvergaard–Needleman (GTN) damage model. Static, dynamic, modal, and harmonic response analyses identify critical stress concentrations at the contact interface between the rolling elements and the outer ring, with maximum deformation occurring in the inner ring. Modal analysis excludes resonance as a potential failure cause. Crack initiation and propagation studies reveal that cracks predominantly form at the pocket bridge corners, propagating circumferentially. The propagation angle increases under circumferential and coupled loading conditions while remaining constant under longitudinal loading. Based on the GTN model, this study is the first to examine the crack propagation and fracture toughness of the cage under various loading conditions. The results indicate that longitudinal loading (Load II) yields the highest fracture toughness, significantly surpassing those under circumferential (Load I) and coupled loading (Load III). Load II exhibits the strongest crack growth resistance, with a peak CTODc of 0.598 mm, attributed to plastic strain accumulation. Fracture toughness decreases with crack depth, as CTODc declines by 66.5%, 20.1%, and 58.4% for Loads I, II, and III, respectively. Crack deflection angles show the greatest variation under Load I (35% increase), while Loads II and III demonstrate minimal sensitivity (<10% change). The optimization of the bearing cage pocket hole fillet radius from 0 mm to 0.75 mm demonstrates a maximum stress concentration reduction of 38.2% across different load conditions. This work introduces a novel methodology for predicting cage fracture behavior and optimizing design, offering valuable insights to enhance the reliability and longevity of systems in high-speed, high-load applications. Full article
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24 pages, 10171 KB  
Article
Analysis of Skidding Characteristics of Solid-Lubricated Angular Contact Ball Bearings During Acceleration
by Shijie Zhang, Yuhao Zhao, Jing Wei and Yanyang Zi
Lubricants 2025, 13(5), 218; https://doi.org/10.3390/lubricants13050218 - 14 May 2025
Viewed by 606
Abstract
Solid-lubricated rolling bearings are widely used in the aerospace field and are key components to support spacecraft rotors. During the start-up of the engine, the sharp acceleration may cause bearing skidding, resulting in damage of the solid lubricating film and a reduction in [...] Read more.
Solid-lubricated rolling bearings are widely used in the aerospace field and are key components to support spacecraft rotors. During the start-up of the engine, the sharp acceleration may cause bearing skidding, resulting in damage of the solid lubricating film and a reduction in the remaining useful life of the bearing. However, the existing research on the tribo-dynamic responses of solid-lubricated ball bearings mostly relies on semi-empirical tribological models, which are limited in their ability to reveal the micro–macro sliding mechanisms of the ball–raceway contact interface. In this paper, a novel tribo-dynamic model for solid-lubricated angular contact ball bearings is developed by applying Kalker’s rolling contact theory to the Gupta dynamic model. The interpolation method is adopted to calculate contact parameters to improve the model’s efficiency. Using the proposed model, the dynamic response of the bearing in the acceleration process is studied, and the mechanism and influence characteristics of skidding, over-skidding, and creepage of the rolling element are analyzed. The results show that the main reason for skidding is that the traction force is not enough to overcome the resistance, and the gyroscopic effect is the main cause of over-skidding, which follows the principle of conservation of the angular momentum of the ball. Full article
(This article belongs to the Special Issue Tribological Characteristics of Bearing System, 3rd Edition)
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29 pages, 1122 KB  
Review
Trends in Lubrication Research on Tapered Roller Bearings: A Review by Bearing Type and Size, Lubricant, and Study Approach
by Muhammad Ishaq Khan, Lorenzo Maccioni and Franco Concli
Lubricants 2025, 13(5), 204; https://doi.org/10.3390/lubricants13050204 - 6 May 2025
Cited by 1 | Viewed by 1288
Abstract
A tapered roller bearing (TRB) is a specialized type of bearing with a high load-to-volume ratio, designed to support both radial and axial loads. Lubrication plays a crucial role in TRB operation by reducing friction and dissipating heat generated during rotation. However, it [...] Read more.
A tapered roller bearing (TRB) is a specialized type of bearing with a high load-to-volume ratio, designed to support both radial and axial loads. Lubrication plays a crucial role in TRB operation by reducing friction and dissipating heat generated during rotation. However, it can also negatively impact TRB performance due to the viscous and inertial effects of the lubricant. Extensive research has been conducted to examine the role of lubrication in TRB performance. Lubrication primarily influences the frictional characteristics, thermal behavior, hydraulic losses, dynamic stability, and contact mechanics of TRBs. This paper aims to collect and classify the scientific literature on TRB lubrication based on these key aspects. Specifically, it explores the scope of research on the use of Newtonian and non-Newtonian lubricants in TRBs. Furthermore, this study analyzes research based on TRB size and type, considering both oil and grease as lubricants. The findings indicate that both numerical and experimental studies have been conducted to investigate Newtonian and non-Newtonian lubricants across various TRB sizes and types. However, the results highlight that limited research has focused on non-Newtonian lubricants in TRBs with an Outer Diameter (OD) exceeding 300 mm, i.e., those typically used in wind turbines, industrial gearboxes, and railways. Full article
(This article belongs to the Special Issue Tribological Characteristics of Bearing System, 3rd Edition)
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19 pages, 14463 KB  
Article
Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism
by Yuchen Yang, Chunsong Han, Guangtao Ran, Tengyu Ma and Juntao Pan
Actuators 2025, 14(5), 218; https://doi.org/10.3390/act14050218 - 28 Apr 2025
Cited by 1 | Viewed by 634
Abstract
This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical parameter selection in VMD, OCSSA adaptively optimizes the decomposition [...] Read more.
This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical parameter selection in VMD, OCSSA adaptively optimizes the decomposition parameters (penalty factor α and mode number K) through a hybrid strategy that combines chaotic initialization, Osprey-inspired global search, and Cauchy mutation. Subsequently, the BiTCN captures bidirectional temporal dependencies from vibration signals, while the attention mechanism dynamically filters critical fault features, constructing an end-to-end diagnostic model. Experiments on the CWRU dataset demonstrate that the proposed method achieves an average accuracy of 99.44% across 10 fault categories, outperforming state-of-the-art models (e.g., VMD-TCN: 97.5%, CNN-BiLSTM: 84.72%). Full article
(This article belongs to the Special Issue Intelligent Sensing, Control and Actuation in Networked Systems)
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