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In industry, rolling bearing damage acoustic emission (AE) signals are interfered with by complex transmission paths and strong noise. The signal-to-noise ratio of the AE signal is low. The bearing periodic fault pulse is weak, and fault feature extraction is challenging. To address these issues, combined with the characteristics of impulsiveness and rapid attention of the AE signal, an enhancement of the bearing weak fault signal based on the autocorrelation function (ACF) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method is proposed in this contribution. Firstly, in low signal-to-noise ratio, the target vector of the MOMEDA method is not optimal, and the diagnostic accuracy is low. To address this problem, this paper improves MOMEDA by using the gradient descent method, called GMOMEDA. Rolling bearing fault AE pulse signals are enhanced. Then, a method combination of ACF and GMOMEDA highlights the periodic elastic wave in the signal. Finally, the enhanced AE signal is processed by envelope demodulation to extract the frequency of the bearing fault signal. The experimental results show that the performance of the ACF-GMOMEDA method is better than the other five methods. The frequency features of bearing fault AE signal can be accurately extracted. [ABSTRACT FROM AUTHOR]

Support matrix machine (SMM) is a matrix classification method that effectively utilises information between rows and columns in matrix-form data. However, the presence of abnormal data in the feature matrix fed into the model can result in model non-convergence and hyperplane shift, greatly affecting classification accuracy. To address this issue, this paper introduces a matrix classification method, called asymmetric robust least squares support matrix machine (ARLSSMM). To construct ARLSSMM, a novel optimisation model based on a matrix with asymmetric constraints is designed to obtain the hyperplane, which can not only use the data set to dynamically determine the position of the hyperplane to obtain the maximum margin space but also reduce the influence of abnormal data on the model classification performance. Two different roller bearing types of test data are used for experimental validation, and the results show that ARLSSMM demonstrates excellent classification performance. [ABSTRACT FROM AUTHOR]

Ball and roller bearings are widely used in the rotor systems of aero-engines. Considering multi-spool engines, the misalignment between the shafts can overload the bearings, lead to their failure, and shorten useful life. In this study, an original and unified nonlinear restoring force model for roller bearings and ball bearings that accounts for misalignment faults is proposed. The non-uniform deformation of the rollers due to misalignment is obtained using the slicing method, while the ball bearings experience changes in bearing parameters. Furthermore, the excitation caused by misalignments in couplings and bearings is considered. A dynamic model of the aero-engine dual-rotor system coupled in the form of a shared bearing bore is developed using the finite element method. A designed on purpose rotor system test bench with roller bearings is set up, and the correctness of the authors' original dynamics model of misaligned roller bearing is verified by experiments. Finally, the effects of different factors such as speed and misalignment angles on the nonlinear vibration response of the dual-rotor system are analyzed. [ABSTRACT FROM AUTHOR]

Crowned rollers of radial bearings are investigated by determining the contact parameters of two cylinders with misalignment. Formulas are derived for the radii of two types of convex (barrel-shaped) roller surfaces and the contact stress. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has inaugurated new approaches to implement fault diagnosis of roller bearings, which is essential to deal with the current industrial big data era. Unfortunately, many existing deep learning models, particularly convolutional neural network (CNN) models, have the following drawbacks. Single-channel convolution neural network in pooling layer has the problem of data loss. The feature information extracted by CNN is not integrated due to lack of feature learning ability, which will induce unsatisfactory diagnostic results and poor generalization ability. To deal with the above issues, a dual-flow convolutional neural network (DFCNN) is proposed for intelligent diagnosis the faults of roller bearings. The multi-channel convolutional neural network is used for feature extraction to solve the problem of data loss in the pooling layer. More comprehensive fault features can be extracted by multiple feature fusion after the input of two dimensions. Network parameters are adjusted using model optimization. The proposed method is demonstrated by the bearing experiment and collected vibration data including different types of faults. The results show that the accuracy rate is more superior than other six different neural network, and it is suitable for roller bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

When the lubricant is not enough to maintain the ideal lubrication state of the bearing, the deep groove ball bearing will enter the starved lubrication state, which leads to the change of the vibration response of the deep groove ball bearing with localized defects. Previous bearing dynamics models mainly consider the lubrication state as an ideal lubrication condition, which may not reveal the effect of starved lubrication on the vibration characteristics of defective bearings. In this paper, the internal interaction of the system under starved condition is taken into account, and a dynamic model of defective deep groove ball bearings with insufficient lubricant is established to investigate the effects of different starved lubrication degrees on the contact force, friction force, cage speed, and system vibration characteristics. The results show that the lubricant insufficiency significantly changes the contact force inside the bearing, and the increase in friction caused by the increase in the degree of starved lubrication leads to the suppression of the degree of bearing slipping. The root mean square (RMS) value of the time-domain signal and the outer ring fault frequency both increase with the increase of the starved degree. The results of the study are helpful for fault diagnosis and condition monitoring of related equipment. [ABSTRACT FROM AUTHOR]

Traditional machine learning techniques depend on both the training data and the target data having the same data distribution and feature space. The performance may be unsatisfactory if there is a discrepancy in data distribution between the training data and the target data, which is known as a cross-domain learning issue. In this study, a technique for addressing this issue based on using the envelope analysis features coded as inputs to the Artificial Neural Networks (ANN) is presented. Specifically, three major cross-domain difficulties were resolved, including defect identification of different roller bearing types, rotating speeds, and loading conditions. The envelope approach is used to extract more distinguishing features at fundamental fault frequencies from the original signal, with the benefit of derived features being independent of both roller bearing type and operating conditions of rotating machines. The diagnosis outcomes were achieved experimentally through three different types of rollers bearing. Meanwhile, for each bearing type, three data sets were obtained at three different rotational speeds with different levels of fault categories to simulate all the cross-domain tasks. Moreover, the ANN model was trained using data of a particular bearing type and a certain operating state, whereas data of other bearing types were utilized to test the cross-domain performance of the proposed technique. At the same loading condition, the results indicate a 99.5% average success rate for bearing Koyo 1205, a 98.33% average accuracy for bearing NU 205, and a 97.3% average accuracy for the defective bearing kit. Furthermore, the results indicate that the suggested technique can deliver accurate cross-domain detection. Also, the experimental results proved that the suggested technology is a potential strategy for industrial applications. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays. [ABSTRACT FROM AUTHOR]

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery's health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm's responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm's accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

A deep understanding of oil behavior inside roller bearings is important for the precise design of bearing configurations and oil systems in aircraft engines. The numerical method is employed to track oil distribution inside cylindrical roller bearings with under-race lubrication along the circumference and radial direction, respectively. The results demonstrate that oil distribution along the circumference is periodic with the number of under-race nozzles, and higher rotating speed and lower flow rate would reduce the fluctuation amplitude. It is difficult for oil to flow through the gap between the cage pocket and rollers, and higher oil viscosity would worsen it further. In some extreme cases, the oil volume fraction near the outer race may be lower than 0.7%, causing the risk of lubricating and cooling failure. Thus, more attention should be paid to the outer race of the roller bearing with under-race lubrication, especially during the starting stage of the engine and in cold weather. [ABSTRACT FROM AUTHOR]

Rolling element bearings are commonly employed in rotating machinery to support rotating shafts. These machinery elements are vulnerable to failure due to increased friction and heat, which cause progressive wear on the rolling interaction surfaces of the bearing. The failure of bearings causes an unpredicted shutdown of equipment, leading to an increase in downtime and financial losses. Various condition monitoring techniques have been developed for periodic assessment of failure in roller-bearing systems. The focus of this work is to establish a relationship between surface fatigue wear, film thickness (h) , asperity load ratio (L a) , lubricant film stiffness (K l u b ) and traction coefficient (f) parameters. A periodic assessment of the above-mentioned parameters was made on the test bearing, which was subjected to a fatigue test for 2000 h. The results acquired from the experimental study highlighted that, as surface fatigue wear on the bearing interaction surfaces and lubricant degradation occurred with an increase in fatigue load cycles, the asperity load ratio (L a) and traction coefficient (f) parameters showed a gradual rise in trend which further resulted in a difference in the dynamic behaviour of the bearing system. [ABSTRACT FROM AUTHOR]

Roller bearings are widely used in machinery. Keeping the contact surfaces of bearings properly lubricated strongly mitigates wear effects, improving bearing fatigue life. Therefore, systematically selecting the lubricant fluid that satisfies bearing operation conditions allows the lubricating layer to be able to carry the applied load, the operating temperature, and the roughness on contact. The nonlinear constrained optimization method is used for lubricant selection, seeking to find the fluid that best fits the bearing operation conditions. Roller bearing lubrication is characteristically elastohydrodynamic (EHL); namely, the Hertz theory describes the load applied on the contacts of a cylindrical roller bearing, and the Stribeck condition classifies the tribological condition of the surfaces in contact. This work concludes that roughness and speed are the most important parameters to consider when choosing a fluid lubricant. [ABSTRACT FROM AUTHOR]

Rolling bearings are a common machine element found in rotary machines. The components in the rolling bearing such as the inner race, outer race, rolling element, and cage are the parts that are often damaged. Traditionally spectrum analysis is used to diagnose bearing defects. However, spectrum analysis is not effectively applied to bearings with early defects because the vibration signal generated is dominated by frequency components from other machine elements, so the frequency of bearing defects cannot be observed. This study proposes an alternative method of detecting bearing defects based on vibration signals using machine learning with a decision tree algorithm. This method is more effective than the spectrum analysis method because machine learning is based on feature extraction and pattern recognition of vibration signal data, therefore, providing classification results directly without further analysis. Vibration signals were recorded using an accelerometer mounted on a bearing housing on a test rig. Nine-time domain statistical parameters and six frequency domain statistical parameters were extracted from the vibration signal and then used as input for decision trees. The results show that the decision trees algorithm gives an accuracy of 94.4% for classifying three rolling bearing conditions using the input of 6 selected frequency domain statistical parameters.

Aiming at the problem of the influence of pocket geometric parameters of cylindrical roller bearing with V-shaped pocket on the frictional heat generation characteristics, the influence law of the wall inclination angle of different V-shaped pocket on the frictional heat generation characteristics of the bearing was studied. Firstly, the bearing dynamics model and frictional power loss model were established based on the multi-body dynamics theory and the tribology principle. Then, the influence of different geometric parameters of the V-shaped pocket on the frictional heat generation was analyzed, and the geometric parameters of the V-shaped pocket were optimized by the orthogonal test method. Finally, the frictional heat generation characteristics between bearing components were analyzed based on the optimized V-shaped pocket. The research results indicate that the geometric parameters of the V-shaped pocket have a significant impact on the pocket frictional heat generation. Under the same operating condition, the maximum difference in frictional power loss of the pocket with different wall inclination angles reaches 736.2 W. When the inclination angle of the pocket wall is α=40 °、β=50 °, the pocket frictional heat generation can be reduced effectively. In addition, the frictional heat generation of the pocket keeps increasing with the increase of rotation speed, but the increase of radial load gradually stabilizes the pocket frictional heat generation. Comparing with the optimized V-shaped pocket cylindrical roller bearing and the ordinary circular pocket bearing, the pocket frictional heat generation and total frictional heat generation of the optimized bearing under different operating conditions are lower than that of the ordinary bearing, and the contact force between the roller and pocket of the optimized bearing is also lower than that of the ordinary bearing. [ABSTRACT FROM AUTHOR]

Outside Russia, helical gears mounted on roller bearings are often used in gearboxes. A method is proposed for calculating the rated bearing life, taking account of the axial load. A sample calculation is presented. [ABSTRACT FROM AUTHOR]

The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings. [ABSTRACT FROM AUTHOR]

This article deals with the numerical simulation of an oil–air multiphase flow inside the thrust bearing of a high-performance mixed-flow pump, including both the lubrication effects and the cooling of the oil by the water-cooling system based on spiral piping. The bearing is lubricated by the oil bath method with partially submersed rollers. Very complex full 3D geometry is modelled in all details, but for modelling purposes, the impacts of some model simplifications on the results are tested. The comprehensive CFD analysis is based on fully transient simulations, taking into account the different rotational speeds and different coordinate systems of all rotating components. The oil distribution on the bearing ring and roller walls as well as the oil temperature are discussed in detail. The results demonstrate that the designed cooling system is efficient in keeping the bearing and oil temperatures at safe values to guarantee bearing rating life even at extreme climatic conditions. The simulations present a comprehensive way of solving complex problems of the bearing and its cooling system applicable to engineering practice. The results of the simulations indicate also that the complexity of the computational domain and bearing clearances have a significant impact on the obtained results. [ABSTRACT FROM AUTHOR]

A nonstandard roller bearing with gear engagement is studied. The load on the roller responsible for frictional–thermal failure of the bearing is determined by an analytical method. The stress–strain state of the bearing under load is calculated by means of ANSYS software. The applicability of such bearings is considered. [ABSTRACT FROM AUTHOR]

Bearing is a crucial transmission component of aero-engines under high-speed and heavy-load conditions. To design a roller bearing with high loading capacity and reliability, it is essential to focus on the relationship between the external load state (reaction force) and the internal load distribution (load distribution). Therefore, a measurement method for bearing reaction force using load distribution and radial basis function neural network is presented in this study. Unlike conventional static reaction force measurement methods, both the direction and magnitude of the reaction force are considered in the proposed method without modifications to bearing. First, an experimental system is designed to investigate the load distribution in a roller bearing under different reaction forces using strain variation measurements. Then, a finite element analysis is conducted, and simulation results of the strain variations at three interested points match well with the experimental measurements. Finally, a radial basis function neural network with strong nonlinear fitting ability is applied to construct the mapping relationship between strain variation and reaction force. The results demonstrate that the proposed method can predict the reaction force with high accuracy based on the strain variation at three measuring points. [ABSTRACT FROM AUTHOR]

In modern engineering applications, rolling element bearings are the essential machine components in rotating machinery, which are used to constrain the desired motion and reduce friction between two rolling/sliding members of machines. Under the normal operating conditions, increase in the fatigue load cycles on the bearing contact surfaces result in surface fatigue faults viz. micro pitting, macro pitting, spalling and scuffing thereby causing damage to roller bearing components. These defects result a gradual increase in vibration levels, which lead to negative impact on the performance of rotating machinery. Hence, in order to maintain better operating performance of rotating machines it is essential to keep a track on operating parameters of roller bearing. In the present work, the experiments were carried out to assess wear propagation on contact surfaces of the roller bearing by using tribological and vibration parameters such as vibration signals, lubricant film thickness, shear rate of lubricant and wear propagation on the bearing contact surfaces. Dowson and Higginson's equation was used to estimate the specific film thickness between rolling elements of the bearing. Results obtained from the experimental investigations provided a good correlation between an increases in surface fatigue wear and corresponding effects on transition in lubrication regimes, increase in vibration levels, variations in rheological properties of lubricant and wear severity developed on the contact surfaces of the roller bearing. The proposed approach can be used as a promising tool to asses surface fatigue wear developed on roller bearing contact surfaces. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the development of technology and policy support, high-speed rail's temporal and spatial layout is gradually expanding, and it becomes essential to ensure high-safety operation. OBJECTIVES: The real-time correlation fault diagnosis technology of critical components of electromechanical systems of high-speed trains is analyzed, and a new method of automatic fault diagnosis based on genetic support vector machine is proposed. METHODS: In this study, the Author combines two techniques, IFD and AE, and introduces an adaptive weighting algorithm to fuse the data of the two and experimentally verify their accuracy. RESULTS: The experimental results show that in the IFD experiment, the 2-point frequency at 1050 speed is 347.6 Hz, and the 3-point frequency is 498.4 Hz, both of which are very close to the 2 and 3 times frequencies of the 1-point frequency, and the multiplicative relationship is much more straightforward. CONCLUSION: Combining IFD and AE can realize automatic and accurate diagnosis of bearing state and pre-diagnosis of bearings by adaptive weighted fusion algorithm, which is effective in the practical mechanical diagnosis of rolling bearing faults in high-speed railroads.

A deep understanding of oil behavior inside roller bearings is important for the precise design of bearing configurations and oil systems in aircraft engines. The numerical method is employed to track oil distribution inside cylindrical roller bearings with under-race lubrication along the circumference and radial direction, respectively. The results demonstrate that oil distribution along the circumference is periodic with the number of under-race nozzles, and higher rotating speed and lower flow rate would reduce the fluctuation amplitude. It is difficult for oil to flow through the gap between the cage pocket and rollers, and higher oil viscosity would worsen it further. In some extreme cases, the oil volume fraction near the outer race may be lower than 0.7%, causing the risk of lubricating and cooling failure. Thus, more attention should be paid to the outer race of the roller bearing with under-race lubrication, especially during the starting stage of the engine and in cold weather.

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery’s health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm’s responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm’s accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm’s effectiveness.

The subsurface cracks in the raceway of a roller bearing will change the contact characteristics between the roller and raceway, which can affect the operational performances of the bearing. Aiming at this problem, the effects of the length, width and slant angle of subsurface cracks on the stress distribution in the crack zone were studied. Firstly, by using the finite element method, a finite element（FE） model for the roller-raceway contact was presented, the calculation results from the finite element model and the Hetzian contact theory were compared to verify the correctness of the finite element model. Then, based on the morphological characteristics of the subsurface cracks in cylindrical roller bearings, a finite element calculation model with subsurface cracks of roller-raceway contact was proposed. Finally, based on the finite element model, the effects of the length, width, and slant angle of the subsurface crack on the stress distribution around the subsurface crack zone were discussed. The research results show that the depth and width of the stress zone increase with the subsurface crack width. The depth and width of the stress zone decrease with the subsurface crack depth. Moreover, for the subsurface cracks with the same height and width, the stress zone decreases with the slant angle of the subsurface crack; and the subsurface crack with a larger slant angle has a smaller effect on the contact characteristics. [ABSTRACT FROM AUTHOR]

Bearing faults are one kind of primary failure in rotatory machines. To avoid economic loss and casualties, it is important to diagnose bearing faults accurately. Vibration-based monitoring technology is widely used to detect bearing faults. Graph signal processing methods promising for the extraction of the fault features of bearings. In this work, graph multi-scale permutation entropy (MPEG) is proposed for bearing fault diagnosis. In the proposed method, the vibration signal is first transformed into a visibility graph. Secondly, a graph coarsening method is used to generate coarse graphs with different reduced sizes. Thirdly, the graph’s permutation entropy is calculated to obtain bearing fault features. Finally, a support vector machine (SVM) is applied for fault feature classification. To verify the effectiveness of the proposed method, open-source and laboratory data are used to compare conventional entropies and other graph entropies. Experimental results show that the proposed method has higher accuracy and better robustness and de-noising ability.

Roller bearings operating under light loads may suffer from skidding. The latter is more pronounced as the speed increases and may result in extensive wear and reduced service life of the bearing. The main objective of this study is to analyze skidding occurrence in a custom-made all-steel M50 bearing and a hybrid roller bearing subjected to the same operating conditions. The test bearings have a bore diameter of 90 mm, an outer diameter of 125mm and 26 rollers. The hybrid bearing features silicon nitride (Si3N4) rollers and steel rings (M50 outer ring and M50-NIL inner ring). The bearings feature special channels to allow for under race lubrication of the bearing components in relative motion. A suite of sensors were used and installed to detect skidding and monitor the overall performance of the test bearing such as vibrations and temperature. The effect of the radial applied load, shaft rotational speed, and oil flowrate on roller skidding has been investigated and discussed. Slip ratio for each of the bearings under various speed-load combinations was also determined and analyzed. Turbo-Oil 2380 was used to lubricate the test bearings at a constant temperature of 77 °C. Tests were conducted on a high-speed rolling bearing test rig, and data were recorded using a 24-bit data acquisition system. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The occurrence of the motor shaft voltage and bearing current caused by the inverter will aggravate bearing damage and lead to the premature failure of bearings. Many types of equipment are being shut down due to bearing currents, such as filters, insulated bearings and grounding brushes. Traditional suppression measures cannot eliminate the bearing current and the bearing damage mechanism under the bearing current is not clear. In this paper, the damage caused by the bearing current to bearings is analyzed in detail. The influences of different working conditions on the bearing current and the damage caused are discussed. The source of bearing currents is introduced and the bearing current model under different working conditions is reviewed. An outlook for future studies is proposed, based on the current research status and challenges. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Roller bearings are important rotating components of engines and have been widely used in the commercial, aerospace, railway transportation, and military fields. The inner race of a bearing that operates at high speeds can attain a temperature of almost 200 °C. Therefore, accurately measuring the speed of bearing inner race under a high-temperature thermal load is vital to ensure that engine is working properly. Herein, we propose a wireless method for measuring the bearing speed. The proposed method has a higher temperature application range and better accuracy than previous used methods. It measures the bearing’s inner race speed based on the eddy current effect and impedance matching. Adding a silver/copper layer to the surface of the bearing substrate changes its conductivity, magnetic permeability, and the equivalent impedance of the antenna. After passing through the coupler and detection circuit, the signal is output to the oscilloscope as a periodic voltage. The rotational speed of the bearing’s inner ring is measured by calculating the periodicity of the waveform. A bearing speed test platform is built and used for multiple groups of bearing speed measurements at 20 °C, 100 °C, and 200 °C on the platform. The results indicate that the method provides excellent stability and accuracy in the range of 0–1000 rpm, with a stable relative error of less than 1.64% and a maximum hysteresis of 1.72%. The maximum repeatability is 1.63658% (1000 rpm, 200 °C), and the maximum uncertainty is 0.66782 (1000 rpm, 200 °C). [ABSTRACT FROM AUTHOR]

The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings.

This article deals with the numerical simulation of an oil–air multiphase flow inside the thrust bearing of a high-performance mixed-flow pump, including both the lubrication effects and the cooling of the oil by the water-cooling system based on spiral piping. The bearing is lubricated by the oil bath method with partially submersed rollers. Very complex full 3D geometry is modelled in all details, but for modelling purposes, the impacts of some model simplifications on the results are tested. The comprehensive CFD analysis is based on fully transient simulations, taking into account the different rotational speeds and different coordinate systems of all rotating components. The oil distribution on the bearing ring and roller walls as well as the oil temperature are discussed in detail. The results demonstrate that the designed cooling system is efficient in keeping the bearing and oil temperatures at safe values to guarantee bearing rating life even at extreme climatic conditions. The simulations present a comprehensive way of solving complex problems of the bearing and its cooling system applicable to engineering practice. The results of the simulations indicate also that the complexity of the computational domain and bearing clearances have a significant impact on the obtained results.

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conditions for seizing during hydrodynamic contact between parts under rolling friction have been studied. For this purpose, a known friction model based on the generalized Eyring equation and taking into account the phenomenon of oil film self-heating has been used. The fact that a correct description of friction curves using this model is possible has been confirmed by comparison with published experimental data. Conditions are revealed, under which in the thermal zone of friction curves, despite a decrease in the friction coefficient, an increase in friction power loss continues. It is shown that the bearing seizing hazard occurs when the bearings operate in a thermal zone of the friction curves. A concept of critical slide–roll ratio that represents a boundary of the thermal zone of friction curves has been introduced, and its dependence on the contact pressure and temperature has been studied for PAO1 grade polyalphaolefin oil in the temperature range of 40–100°C and for MN-7.5u grade oil in the range of 60−140°C. It is shown that the critical sliding-to-rolling ratio increases with decreasing contact pressure in the bearing, as well as with increasing oil temperature. It is concluded that the conditions of contact between the rollers and the inner ring promote an increase in friction power loss to a greater extent than the conditions of contact between the rollers and the outer ring. This indicates a higher seizing hazard in the case of the inner ring than in the case of the outer ring, which is confirmed by the statistics of rolling bearing failures. In order to prevent seizing in bearings, it is necessary to provide a margin with respect to critical sliding at the bearing design stage. [ABSTRACT FROM AUTHOR]

• A novel Robust Smooth Constrained Matrix Machine (RSCMM) is proposed. • RSCMM model can reduce the impact of noise on the decision hyperplane through the RoBoSS loss term. • RSCMM model can handle situations of imbalanced data by dynamic adjustment factor. • RSCMM has superior classification performance in roller bearing fault diagnosis. In mechanical health management, the issue of data imbalance is frequently encountered, particularly in cases where there is an extreme excess or scarcity of data. Although support matrix machine (SMM) can effectively transmit and classify structured information through matrix transmission, its effectiveness remains limited when faced with the challenge of data imbalance. Additionally, the SMM model is sensitive to noise and exhibits slow training convergence, both of which further undermine the classification performance of the model. To address the above problems, a method for the diagnosis of roller bearings, termed robust smooth constrained matrix machine (RSCMM), is proposed in this paper. Firstly, a dynamic adjustment factor is designed in the RSCMM model, which can dynamically adjust the coefficients of the loss term according to the sample imbalance ratio, and improves the performance of recognizing imbalanced data. Then, the RoBoSS loss term is employed in the model, mitigate the impact of noise and improve the convergence speed and robustness. Finally, the effectiveness of the proposed method is verified by two roller bearing experimental datasets. The experimental results show that RSCMM performs well under different imbalance ratios, which demonstrates that RSCMM has excellent performance in roller bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

A multicycle focal spot mapping method is proposed to design the specific pulse emission frequency and the recorded pulse number. A great number of successive ultrasonic pulses with a specific emission frequency are utilized to examine the lubricant film in the roller bearing so that at least one ultrasonic pulse will interrogate the center of the contact region. Subsequently, the center oil film thickness is calculated by the selected minimum ultrasonic reflection coefficient amplitude. A statistical test analysis and an experimental analysis are performed to validate the proposed method. The results show that the proposed method can be used to accurately measure the center oil film thickness in high-speed roller bearings, which overcomes the speed limitation of the existing method. • Ultrasonic measurement method for center film thickness in high-speed roller bearing. • Multicycle focal spot mapping method for designing the specific pulse emission frequency. • A great number of successive ultrasonic pulses for interrogating the center of the contact region. [ABSTRACT FROM AUTHOR]