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Rolling bearing operation under mixed and boundary lubrication conditions may lead to heavy adhesive or abrasive wear, which may lead to wear-induced rolling bearing failure. The purpose of this paper is to investigate the wear protection capabilities of different grease compositions at varying temperatures. It is considered that the temperature influences the lubrication conditions, the behaviour of grease components, namely, bleed oil and thickener, as well as the tribofilm formation due to tribo-chemical interactions between additives and surfaces.

In this study, four different greases were produced on the basis of a mineral base oil by varying the thickener and the addition of ZDDP. Various grease-lubricated rolling bearing experiments were conducted in a wide temperature range from 0°C to 120°C. Subsequently, the wear pattern, tribofilm formation and grease structures were analysed. Thereby, the influence of the different grease thickeners and the performance of ZDDP as a common antiwear and extreme pressure additive was evaluated.

The results show a strong temperature-dependency and allow a classification of temperature ranges concerning wear protection. At low temperatures, all greases provide a very good wear protection without the evidence of additive-based tribofilm formation. In the experiments at elevated temperatures, ZDDP tribofilms were formed. The formation depends on the thickener type: in comparison to lithium thickener, polyurea thickener favours more protective tribofilms at the same temperature. The experimental results show that medium temperatures in the range of 40°C–60°C are critical concerning wear due to the insufficient tribolayer formation and limited load carrying capacity of the grease.

Temperature is a key operating parameter for grease lubrication in roller bearings. The experimental work enables consideration of different impact pathways of temperature by combining roller bearing tests and microanalysis.

This paper aims to modify carbon nanotubes with oleic acid, and to study the tribological properties of castor oil with modified carbon nanotubes additives. The proper additives are sought for the future engineering application of castor oil.

Tribological properties of the castor oils mixed with the modified carbon nanotubes of four mass percentages were investigated using a four-ball testing rig. Coefficient of friction and wear scar diameter were obtained in each test, and the mechanism of modified carbon nanotubes and castor oil was discussed.

The results indicated that modified carbon nanotubes had better dispersion in castor oil. Coefficient of friction first increased, then decreased and finally grew stable with the time, and wear scar diameter of steel surface functioned as a first reduced then increased change with the additive mass percentage of modified carbon nanotubes. The minimum of average coefficient of friction and wear scar diameter occurred at 0.02 Wt.% modified carbon nanotubes.

A small amount of modified carbon nanotubes could improve properties of the castor oil, and the mixed castor oil with 0.02 Wt.% modified carbon nanotubes would be most possibly used in engineering applications.

The purpose of this paper is to study the film-forming capacity of logarithmic crowned roller for tapered roller bearing (TRB) and to design a tapered roller profile based on an elastohydrodynamic lubrication model.

A coupled model, incorporating a quasi-static model of TRBs and an elastohydrodynamic lubrication model was developed to investigate the load distribution of TRB and to evaluate the lubrication state of tapered roller/raceway contact.

The model is verified with published literature results. Parametric analysis is conducted to investigate the effect of crown drop on azimuthal load distribution of the roller, film thickness and pressure distribution in the contact area. The result shows that crown drop has little influence on the azimuthal load distribution; also, the film thickness and the pressure distribution are asymmetric. When the tapered roller is designed and manufactured, the crown drop of the small end should be larger than that in the large end.

Precise roller profile design is conducive to improve the fatigue life of TRBs. Currently, most crown design methods neglect the influence of lubrication, which can lead to a non-suitable roller profile. Therefore, the present work is undertaken to optimize roller profiles based on lubrication theory.

The purpose of this paper is to introduce a high-speed rolling bearing test rig supported by sliding bearing and its first experimental results.

Through analyzing the disadvantages of using rolling bearing as supporting bearing, the bottlenecks that need to be resolved urgently in the development of rolling bearing experimental technology, and the advantages of the sliding bearing, this study used the sliding bearing as the supporting bearing for the high-speed rolling bearing test rig for the purpose of prolonging the service life, increasing the load capacity and promoting the operating stability.

The experimental results show that the high-speed rolling bearing test rig supported by sliding bearing could stably rotate at 70,800 rpm without installing the test bearing; the temperature of the sliding bearing is increasing with the rotating speed and the maximum is less than 95°C. Moreover, the new test rig, installing an angular contact ball bearing as test bearing, could also stably rotate at 54,000 rpm with 2 kN axial load and 1 kN radial load; the temperature of the sliding bearing is increasing with the rotating speed and the maximum temperature is less than 97°C.

Rolling test rig has been established.

This paper proposes a high-speed rolling bearing test rig supported by sliding bearing, which greatly prolongs the service life, increases the load capacity and promotes the operating stability, moreover, reduces the risk of supporting bearing failure before the test bearing. This paper can also provide a new idea and reference for the design of similar bearing test rig.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0085/

The purpose of this paper is to investigate the non-linear characteristics and stability of the rolling bearing–axle coupling system under the excitation of the axle/wheel speed of railway freight cars, so as to put forward a rationale for judging the vibration law and running stability of railway freight wagon.

Considering the effects of eccentric force of the railway wagon axle, the non-linear resistance of the wagon and non-linear support forces of axle box rolling bearings, a centralized mass model of rolling bearing-axle coupling system of railway freight wagon is presented on the basis of the theory of rotor dynamics and non-linear dynamics. Then the Runge-Kutta method is adopted to solve the non-linear response of the proposed system, and numerical simulation including bifurcation diagrams, axis trajectory curves, phase plane plots, Poincaré sections and amplitude spectras are analysed when the axle rotating speed is changed. Meantime, the relation curve between Floquet multiplier and axle rotating speed, which affects the stability of coupling system, is plotted by numerical method based on the Floquet theory and method.

The simulation results of the dynamic model reveal the abundant dynamic behaviour of the coupling system when the axle rotating speed changes, including single period, quasi period, multi-period and chaotic motion, as well as the evolution law from multi-period motion to chaotic motion. And especially, the bearing–axle coupling system is in stable state with a single period motion when the axle rotating speed changes from 410 rpm to 510 rpm, in which the running speed of railway freight wagon is changed from 62 km/h to 80 km/h, the vibration displacement of the coupling system in X direction is between 1.2 mm and 1.8 mm, and the vibration displacement of the coupling system in Y direction is between 1.0 mm and 1.45 mm. Meanwhile, the influence law of axle rotating speed on the stability is obtained by comparing the bifurcation diagram and Floquet multiplier graph of the coupling system.

The numerical simulation data obtained in this study can provide a theoretical evidence for designing the running speed of railway freight wagon, utilizing or controlling the non-linear dynamic behaviours of the proposed coupling system, and ensuring the stability of railway freight wagons.

Rolling bearings often cause engineering accidents due to early fatigue failure. The study of early fatigue failure mechanism and fatigue life prediction does not consider the integrity of the bearing surface. The purpose of this paper is to find new rolling contact fatigue (RCF) life model of rolling bearing.

An elastic-plastic finite element (FE) fatigue damage accumulation model based on continuous damage mechanics is established. Surface roughness, surface residual stress and surface hardness of bearing rollers are considered. The fatigue damage and cumulative plastic strain during RCF process are obtained. Mechanism of early fatigue failure of the bearing is studied. RCF life of the bearing under different surface roughness, hardness and residual stress is predicted.

To obtain a more accurate calculation result of bearing fatigue life, the bearing surface integrity parameters should be considered and the elastic-plastic FE fatigue damage accumulation model should be used. There exist the optimal surface parameters corresponding to the maximum RCF life.

The elastic-plastic FE fatigue damage accumulation model can be used to obtain the optimized surface integrity parameters in the design stage of bearing and is helpful for promote the development of RCF theory of rolling bearing.

The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain.

The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification.

The experiment results show a high fault classification accuracy based on the proposed method.

The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.

The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy.

The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN.

CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%.

This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

A LUBRICATING grease has been defined by the ASTM (American Society for Testing and Materials) as a solid to semifluid lubricant consisting of a thickening agent in a liquid lubricant. Other ingredients imparting special properties may be included.