Blade bearings of wind turbines are grease-lubricated rolling bearings which are operated in an oscillating manner. They can be subjected to both rolling contact fatigue and wear, in particular standstill marks and false brinelling. Rolling contact fatigue arises from alternating stresses due to the passage of rolling elements under high loads, standstill marks and false brinelling occur during cyclic motion and are often exacerbated by inadequate lubrication. In this paper, we want to understand fatigue and the influence of false brinelling on fatigue better and to investigate whether it is advantageous to avoid false brinelling. We performed fatigue tests with four blade bearings. Three of them had existing false brinelling damage. In addition, we repeated tests on much smaller angular contact ball bearings. To contextualize the results, we calculated the fatigue life of a pitch bearing from a reference turbine matching our test bearing design and compared the calculation to the test results. Although the stochastic fatigue behavior in combination with the low number of tests makes it difficult to draw general conclusions, the results indicate that false brinelling shortens fatigue life, even if the blade bearings pass the fatigue test.

Hysteretic damping model for the dynamic response in four-point contact slewing bearings

In order to develop the mechanical engineering and road industries, it is necessary to develop, design and manufacture modern high-performance equipment. Road rollers are popular machines in transport construction. They are designed to compact technological layers of roads in order to give strength and stability to the entire road structure.Currently, an urgent task in the production of domestic rollers is to increase their performance characteristics to the values ​​​​of leading foreign manufacturers, while maintaining a sufficient cost of domestic road equipment. In the conditions of high competition and import substitution programs, it is necessary to provide road construction organizations with domestic mechanization tools and load the capacities of machine-building enterprises.As a result of the analysis of the characteristics of compaction equipment of Russian and foreign manufacturers, the advantages and disadvantages of road rollers were identified. One of the disadvantages of existing rollers is the idle component of the work, when the vibratory drum moves upward and does not perform useful compaction work on the material.The new design of the vibratory roller contains a frame and a working element with two rollers - the main vibratory and additional compensating. The rollers are pivotally connected to each other via a slewing bearing. During compaction, the compensating roller moves downwards and compensates for part of the lost energy of the main vibratory roller.The article contains a preliminary calculation of the parameters of the presented new roller design. The dynamic scheme of the roller takes into account the nature of the connection of its rollers with the frame. The elastic and viscous properties of special suspension elements and devices are considered in combination with the elastic-viscous properties of the rollers and the hinge.The article defines rational mass-dimensional characteristics of the original working element of the road machine. The obtained results allow us to consider, as a first approximation, the design parameters for the creation and production of a domestic high-performance road machine for compaction of technological layers during the construction of highways and other transport facilities.

Abstract The fault diagnosis of slewing bearings is crucial for modern industry. However, operational constraints and high signal acquisition costs limit the number of available diagnostic samples, leading to decreased diagnostic accuracy. This study proposes a novel fault diagnosis method for slewing bearings based on audible sound signals, termed Time Generative Adversarial Network (Time GAN)–Tabular Prior-Data Fit Network (TabPFN). It is a hybrid approach that integrates the capabilities of Time GANs and the TabPFN. The method leverages the feature enhancement capabilities of Time GAN and the probabilistic modeling strengths of TabPFN to improve fault diagnosis accuracy. This method utilizes low-cost, easily obtainable audible sound signals as input. By employing Time GAN, the original data features are enhanced, generating new training samples. Subsequently, the TabPFN framework constructs a substantial amount of synthetic data with causal relationships, facilitating Bayesian inference. Experimental results demonstrate that the proposed method effectively identifies various fault types with small-sample sizes, achieving an accuracy of 96.5%, approximately 10% higher than existing algorithms. Furthermore, this method exhibits high diagnostic accuracy and strong generalization capabilities, making it a robust solution for slewing-bearing fault diagnosis.

Abstract Problems of computing a slewing bearings static carrying capacity have been presented in the paper. Particularly it was concentrated on determination of static limited load curves which include axial forces, radial forces and tilting moments. A calculation were performed on the base of single-row ball slewing bearing with four-point contact zone. In this work a procedure of determining the static limiting load curves on the basis of modeling by using the finite element method (FEM), analytic Eschmann’s formulas and classical mechanics equations have been described. The structure of FEM bearings’ model was considered with gear conditions between a toothed bearing’s ring (rim) and a drive pinion in a power train of the excavator F250H symbol. Moreover, in the model flexibility of: the bearing rings, a contact zone ball-bearing, support structure and mutual interactions between bolts clamping the bearing rings and the support structures were taken into account. The static carrying capacity of the analyzed bearing, considered with the pinion and without was compared. Quantitative assessment of loads of the contact zones ball-raceway was achieved by using a statistic criterion.

With the development of industrial technology, lifting equipment has been widely used in various industries. Among them, truck cranes are often chosen in daily use due to their good mobility and rapid transfer. The slewing bearing is an important component in a truck crane that connects the upper and lower parts of the vehicle. Taking a certain model of truck crane as an example, this paper analyzes the contact stress of its slewing bearing by using two methods: Hertz contact theory and finite element method. It also explores the influence of the main structural parameters of the slewing bearing on the contact stress, so as to provide a reference for product selection and design.

As the main bearing component of the tunnel boring machine (TBM), the three‐row roller slewing bearing is required to withstand a heavy load and overturning moment in the tunnelling process. Therefore, a dynamic model of a slewing bearing considering the external load and internal structural parameters is established. First, the influence of the complex load on the internal structural parameters (clearance, radial displacement, roller tilt and skew) of the slewing bearing is analysed. Then, by using the four‐stage Runge–Kutta method, the dynamic model is solved, and the influence of different parameters on the dynamic performance and stability is obtained. Finally, the proposed model is verified via experimental research, and the accuracy error of the model is less than 7%. The state of the inner ring’s motion progressively changes from stable to unstable as the rotational speed increases. The dynamic characteristics of a slewing bearing can be improved by reducing the radial clearance and the number of rollers while it is subjected to excessive eccentric loading. The results of this study provide important guidance for the design of slewing bearing structures.

To investigate four-point contact ball slewing bearings, a bearing support bolt-integrated model was created with HyperMesh and ANSYS software, and its accuracy was theoretically confirmed. This study examines how the rolling element number Z, contact angle α, bolt number N, bolt pre-tightening force coefficient P, and radial load-overturning moment angle θ affect the comprehensive performance of four-point contact ball slewing bearings and connecting bolts. The study found that increasing Z, α, N, P, and θ reduces overall bearing, ring, rolling element, and contact load deformations. The maximum deformation and stress of bolts rise with P but decrease with Z, α, N, and θ. The degree of influence of each parameter on the deformation of the inner and outer rings, the deformation of the rolling element, and the contact load of the rolling body from large to small is ranked as follows: α, N, Z, θ, and P; the degree of influence on bolt deformation and bolt stress distribution uniformity from large to small is ranked as follows: N, α, Z, θ, and P; the degree of influence on the overall deformation of the bearing from large to small is ranked as follows: N, θ, α, Z and P; the degree of impact on the maximum stress of the bolt from large to small is ranked as follows: P, N, Z, α, θ. To improve the overall performance of a four-point contact ball slewing bearing, increase α, N, Z, and θ.

The load capacity and lubrication performance of the slewing bearing are fairly sensitive to edge effects at both ends of the main thrust rollers. Therefore, quantitative investigations of the edge effect at both ends of slewing bearing is practically important. In this study, an elastic fluid lubrication model of the slewing bearing that considered the external load and internal structural parameters is established. A modification function of rollers is included in a finite long-line contact elastic fluid lubrication model to perform the dynamic analysis. The simulated oil film’s thickness and pressure distributions are compared under different working conditions. And the edge effect is eliminated by modifying the rollers’ construction. The results indicate that under the large eccentric load, the speed of the slewing bearing must be decreased to ensure elastic fluid lubrication performance. A genetic algorithm that used to discover the optimal modification length shows the best busbar of the main thrust roller is 12 μm, which could decrease the vibration amplitude of the unmodified roller by 8.13%. This result gives a significant way for slewing bearing’s structural design.

Finite-element analysis is the only means to determine the load distribution of large slewing bearings considering flexible bearing rings and supporting structures. For reliable results, the plausibility of the models need to be validated. Previous attempts on validating a finite-element model of a slewing bearing against measurement results have indicated a huge dependence of the deformation on tolerances in the supporting structures. This dependence has not yet been explored in research in favor of a focus on tolerances of the bearing itself. The present work explores different irregularities of the flange that connects to the outer ring of the bearing and their effects on bearing deformation. The results show that single dents or bulges on the flange and inclined flanges of the adapter ring significantly change the load distribution and contact angles of the bearing. They also aggravate the risk of truncation. For the calculated fatigue life however, the bearings seem to be robust to these uncertainties for the shown load cases. The dimensions of the investigated tolerances are verified by comparing the resulting deformations of the bearing outer ring against experimental data.

Prediction of fatigue life of slewing ball bearings is challenging due to the influence of large deformations and adverse lubrication conditions. The present study presents an elegant simulation model that considers the flexibility of the supporting elements of the slewing bearing along with stick/slip zones at the raceway-ball contact interface. Using the stresses predicted by the simulation model, relative fatigue life using stick/slip condition versus no-friction condition is computed. A sensitivity study of the ball contact force, ball contact angle, coefficient of friction and raceway conformity is also performed. The present study also evaluates the results in comparison with full-sliding assumptions. The final results show that the stick/slip zone determination at the contact interface is beneficial for accurate determination of fatigue life of slewing bearings.

The pitch bearings of wind turbines are slowly oscillating, grease-lubricated slewing bearings. They facilitate the pitching movements of blades which control aerodynamic loads. These bearings have diameters of several meters, their blade-side sealings can face the environment, bending moment loads can cause radial deformation of the bearing rings, and their highly variable operating temperatures can facilitate condensation of water inside them. All of this makes water ingress into the lubricant possible. There is limited public knowledge with regards to the maximum water content for safe operation in this application. This work presents the results of a series of scaled wind turbine time series tests with both ‘dry’ (no water contamination) and ‘wet’ (10 mass % demineralized water added) greases. A set of four commercially available greases were tested. The time series were scaled from wind turbine operation and represented a 13.7 h worst-case scenario of operation with small oscillation amplitudes and no longer lubrication runs in between. Three of the greases showed reduced friction and no or limited raceway damage in the wet condition, whereas one showed increased friction and raceway damages.

It is difficult to obtain the damage information on large slewing bearings only from vibration signals. In addition, deep learning models trained on old samples do not achieve high accuracy in new tasks. Therefore, this paper uses vibration, temperature, and torque signals of slewing bearings to build a model. Meanwhile, we add attention mechanism to capture internal correlation of them to consider the related factors of remaining useful life (RUL) from multiple angles. The multivariable gated recurrent unit (GRU) based on attention mechanism gated recurrent unit (attention-MGRU) model is adopted to improve the prediction performance. On this foundation, a fine-tuning strategy is introduced to improve the generalization ability of the model. A full-life accelerated test is carried out on the slewing bearing test bench. The model proposed in this paper is compared with GRU prediction model, which utilizes vibration signals and multivariable GRU prediction model. Mean absolute error (MAE) and root-mean-square error (RMSE) are used as measurement indicators. Among different methods, three indicators generated by attention-MGRU show significant superiority. Moreover, the fine-tuned model performs better in new tasks compared with the original model.

This manuscript presents an innovative methodology for the assessment of the friction torque of ball slewing bearings. The methodology aims to overcome the limitations of state-of-the-art approaches, especially when the friction torque is conditioned by the preload of the balls. To this end, the authors propose to simulate the preload scatter when solving the load distribution problem, prior to the friction torque calculation. This preload scatter allows to simulate a progressive transition of the balls from a four-point contact state to a two-point contact one. By implementing this capability into an analytical model, the authors achieve a successful correlation with experimental results. Nonetheless, depending on the stiffness of the structures to which the bearing is assembled, it is demonstrated that the rigid ring assumption can lead to inaccurate friction torque results when a tilting moment is applied. The methodology described in this research work is meant to have a practical application. Therefore, the manuscript provides guidelines about how to use and tune the analytical model to get a reliable friction torque prediction tool.

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm's (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA's superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.

Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.

Despite the high technical level of hoisting equipment purchased abroad, a catastrophic failure of the slewing bearings of these cranes occurs after 3-5 years of service instead of the regulated 15 years. The most characteristic are: wear of 6-polyamide cassettes, cages of gear teeth and turntable; rolling elements falling out of the turntable; squeezing out the sealing tape. To identify the hidden cause of premature failure of the slewing rings of «Liebherr» cranes, the authors carried out the experiment during loading and unloading operations, which shows the tendency of warming up the elements of the slewing support, considering the cyclic nature of the crane operation. It has been established that this process is linear both for cassettes (polyamide 6 material), in which the rolling elements are located,and for the rolling elements themselves (bearing steel). For 5 loading cycles, the rolling elements warm up to a 40 mm depth to a temperature of about 40-45° C. The proposed working hypothesis allows identifing the hidden cause of premature failure of the slewing support of «Liebherr» cranes operating in central Russia. A change in the stress-strain state of the cassette is established, leading to an increase in wear of the cassettes and rolling elements, since stresses arise between the end of the roller surface and the cassette in the direction of the contact plane.

Production quality in terms of process efficiency and quality in the manufacturing industry must always be improved in order to maintain customer confidence. PT XYZ as a heavy equipment assembly company is one of the companies that depends on process reliability for a smooth production process. Based on this, the problems raised in this study focus on increasing the efficiency of the process of installing bolts on slew bearings. By using Overall Equipment Efficiency (OEE) and Process Capability (CpK) as the main benchmarks for measuring the quality of the production process where the latest data shows the average OEE value is at 18.53%, while the CpK value for the bolt tightening process is at 0 ,67. The OEE and CpK figures obtained show that the process quality is still not optimal and needs to be improved. The purpose of this research is to identify and prevent as many factors as possible that can lead to process failure. The methods used to evaluate processes and to identify where and how a process might fail are Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis (LTA). Both methods are used to identify and prevent as many factors as possible that can lead to a process failure. The results of research using the FMEA and LTA methods show that in the process of installing slew bearing bolts there is a process that needs to be improved because the RPN value is quite high, namely above 125. Some suggestions for improvement such as the use of a manipulator arm on a torque tool and the implementation of a manufacturing execution system (MES) can reduce the RPN value from above 125 to 28, where the process obtained is better than before.

An engineering approach to assess friction torque in generally loaded four-point contact slewing bearings

Crack propagation analysis of slewing bearings in wind turbines applying a modified sub-model technology