Search Results (328)

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

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

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

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

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

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

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 X_shade 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

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 $r_o$ of 7.8 mm, and an outer race initial polar radius $r_o$ 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 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 CTOD_c of 0.598 mm, attributed to plastic strain accumulation. Fracture toughness decreases with crack depth, as CTOD_c 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

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

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 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 $\alpha$ 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

Rolling element bearings (REBs) are vital in rotating machinery, making fault detection essential for optimal performance and system reliability. This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults using time-domain signals, frequency-domain spectra, and envelope frequency spectrum analysis. The study uses diverse datasets, including laboratory and industrial data under various operating conditions, covering fault types like inner race fault (IRF), outer race fault (ORF), rolling element fault (REF), and healthy (H) states. The main innovation is applying Transfer Learning (TL) with fine-tuning to improve model accuracy in identifying REB conditions by leveraging features learned from diverse datasets. An innovative algorithm is also introduced to identify resonance regions for optimal filter selection in envelope analysis, improving fault-related feature extraction and reducing noise. A preprocessing step that removes speed-related variations further enhances model accuracy by isolating fault features and minimizing the impact of rotational speed. The results show that transfer learning with fine-tuning, combined with the resonance region identification algorithm, significantly enhances fault detection accuracy. The TL-CNN model with envelope signal input achieves the highest accuracy across all scenarios, especially under variable operating conditions, and performs reliably on industrial data. Full article

Due to the harsh and complex operating conditions, rolling element bearings (REBs) are prone to failures, which can result in significant economic losses and catastrophic breakdowns. To efficiently extract weak fault features from raw signals, singular value decomposition (SVD)-based signal denoising methods have been widely adopted in the field of rolling bearing fault diagnosis. In traditional SVD-based methods, singular components (SCs) with significant singular values are selected to reconstruct the denoized signal. However, this approach often overlooks low-energy SCs that contain important fault information, leading to inaccurate diagnosis. To address this issue, we propose a new selection scheme based on frequency domain multipoint kurtosis (FDMK), along with a reweighting strategy based on FDMK to further emphasize weak fault features. In addition, the estimation process of fault characteristic frequency is introduced, allowing FDMK to be calculated without prior information. The proposed FDMK-SVD can adaptively extract periodic fault features and accurately identify the health condition of REBs. The effectiveness of FDMK-SVD is validated using both simulated and experimental data obtained from a locomotive bearing test rig. The results show that FDMK-SVD can effectively extract fault features from raw vibration signals, even in the presence of severe background noise and other types of interferences. Full article

Under mixed lubrication, the macro size is affected by the wear of the surface roughness peaks, which results in degradation of the bearing accuracy. To study the wear characteristics of rolling bearings under mixed lubrication, based on the elastohydrodynamic lubrication theory and Archard wear model, and considering the coupling of the oil film and roughness, a wear prediction model of angular contact ball bearings under mixed lubrication was established, and the influence of the working parameters and hardness on bearing wear was analyzed. The results show that the wear depth of the outer grove increases with an increase in the load, or a decrease in the rotational speed or the initial viscosity of lubricating oil. The load has the most significant effect on the wear depth of the outer grove. There is a critical value for the load, rotational speed, and initial viscosity of the lubricating oil, which varies with the parameters of other working conditions and the hardness of the materials. When the increase in load exceeds the critical value or the rotational speed and initial viscosity of lubricating oil are less than the critical value, the outer groove fails because the wear depth exceeds the critical value of wear depth. The ratio of the load on the rolling element to the hardness of the outer grove at different entrainment speeds and initial viscosities of lubricating oil can be used to predict the wear degree of the outer grove. When the ratio is greater than a certain threshold, the outer grove is faulted owing to wear, and the threshold decreases with an increase in the initial viscosity of lubricating oil or the decrease in rotational speed. Full article