Rolling Element Research Articles

Application of Flores model considering variable recovery coefficient in dynamics analysis of ball bearings

PurposeThis paper aims to select an appropriate contact force model and apply it to the interaction model between the balls and the cage in the rolling bearings to describe the elastic–plastic collision phenomena between the two.Design/methodology/approachTaking the ball–disk collision mode as an example, several main contact force models were compared and analyzed through simulation and experiment. In addition, based on the consideration of yield strength of materials and initial collision velocity, a variable recovery coefficient model was proposed, and its validity and accuracy were verified by the ball–disk collision experiments. Then, respectively, the Flores model and the Hertz model were applied to the interaction between the balls and the cage, and the dynamics simulation results were compared.FindingsThe results indicate that the Flores model has good regression of recovery coefficient, indicating good applicability for both elastic and elastic–plastic contacts and can be applied to the contact collision situations of various materials. Under certain working conditions, there are significant differences in the dynamics results of rolling bearings simulated using the Flores model and Hertz model, respectively.Originality/valueThis paper applies the Flores model with variable recovery coefficients to the dynamics simulation analysis of ball bearings to solve the elastic–plastic collision problem between the rolling elements and the cage that cannot be reasonably handled by the Hertz model.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0138/

Hardware implementation of bearing fault diagnosis using empirical mode decomposition

ABSTRACT This paper presents a comprehensive methodology for developing a vibration measurement system, with a primary focus on monitoring rolling element bearings, widely recognised as pivotal components in generic machine vibration monitoring. Through the utilisation of sensors to capture vibration data signals, the system aims to meticulously analyse these signals to discern any indications of bearing health deterioration, thus facilitating the early identification of potential pre-breakdown conditions. The proposed system initiates the process by internally decomposing the three-axis vibration signal, enabling subsequent transmission of processed data to a remote location for the identification of specific faults in the roller element bearings. Installation of a three-axis accelerometer on the bearing housing, positioned radially, facilitates the capture of raw vibration data along one axial and two radial directions. Moreover, efforts to enhance computational efficiency involve cascading multiple identical modules in a serial pipeline structure, allowing for iterative loop computation of intrinsic mode functions within Empirical Mode Decomposition (EMD). Real-time computation is achieved through this serial pipeline structure, necessitating two distinct clock frequencies: one at 12.5 MHz for sampling and storing 3-axis vibration data in ping pong buffers, and another at 40 MHz for reading and processing the data signal retrieved from these buffers. Validation of the proposed method is demonstrated through simulation results obtained via synthesised Verilog code on Xilinx Artix-7 FPGA (XC7A35T-1CPG236C), affirming its efficacy in real-time applications.

Research Progress on Rolling Forming of Tungsten Alloy.

Tungsten is a metal with many unique characteristics, such as a high melting point, high hardness, high chemical stability, etc. It is widely used in high-end manufacturing, new energy, the defense industry, and other fields. However, tungsten also has room-temperature brittleness, recrystallization brittleness, and other shortcomings due to the adjustment of the composition and organizational structure, such as the addition of alloying elements, adjusting the phase ratio, the use of heat treatment and deformation strengthening, etc. Its performance can be improved to meet the requirements for use in different fields. At present, the main production method of tungsten alloy is powder metallurgy. The use of a rolling open billet rotary forging-stretching process can improve production efficiency and product quality, but in actual production, due to the combined effects of various factors, such as elastic deformation of rolling elements, plastic deformation of the rolled material, etc., the mechanical properties of tungsten plates and bars are often difficult to control effectively, seriously affecting rolling stability and production efficiency. For this reason, researchers have conducted extensive and deep research and optimization on the rolling process of tungsten alloys, including establishing mathematical models, performing numerical simulations, optimizing process parameters, etc., providing important references for the rolling and forming of tungsten alloys. Meanwhile, the material properties are greatly influenced by the microstructure, and the evolution of the microstructure can be well quantified by some advanced characterization techniques, such as SEM, TEM, EBSD, etc., so that certain properties of tungsten can be obtained by controlling the texture evolution. In conclusion, this paper comprehensively summarizes the research progress of tungsten alloy roll forming and provides an important reference for further improving the processing performance and production efficiency of tungsten alloy.

Evaluation and analysis of abrasive wear resistance of hybrid roller bearings under lubricant contamination

Abrasive wear is a common failure mode of rolling bearings. To enhance the resistance of traditional all-steel bearings to abrasive wear, the structure of traditional all-steel cylindrical thrust roller bearings has been appropriately adjusted, and hybrid roller bearings have been formed by replacing some of the steel rollers in the traditional bearings with ceramic rollers. The frictional performance of hybrid roller bearings under lubricant contamination conditions was analyzed, and the wear reduction mechanism was discussed. The results show that under the condition of lubricant contamination, replacing the appropriate amount of ceramic rollers not only helps reduce friction and decrease wear but also contributes to improving the overall temperature rise of the bearing. Furthermore, when the total amount of contaminants is constant, a specific threshold exists for the impact of replacing the number of rolling elements on reducing mass loss. For a substantial reduction in wear with hybrid roller bearings, the replacement rollers should constitute at least one-fifth of the total number of rollers. The wear reduction observed in hybrid roller bearings results from a combination of crushing and refinement, grinding and finishing, and self-healing mechanisms.

Influence of lubrication state transition on dynamic characteristics of deep groove ball bearing with localized defect in thermal environment

The accuracy of the vibration response in a defective bearing dynamics model depends on the precise representation of the lubrication friction state between the rolling element (RE) and the raceway within the model. In this study, a fault dynamic model for outer ring defect of deep groove ball bearings (DGBBs), considering the lubrication state transition in a thermal environment, is established. This model accounts for the asperity contact effect during the lubrication state transition and integrates the lubrication-friction model for temperature changes into the dynamic model when skidding occurs. The influence of lubrication state change on the fault frequency of outer ring of DGBB in thermal environment is studied. The experimental and simulation results indicate that the lubrication state of the bearing is gradually deteriorated from elastohydrodynamic lubrication to mixed lubrication with the increase of working temperature. The transformation of the lubrication state is shown to have a significant effect on friction, resulting in the fault frequency of the outer ring increasing with temperature, which exhibits substantial deviation in the thermal environment. In the temperature range of 30 °C–150 °C, the deviation of defect frequency reaches 15.5%, which affects the accuracy of bearing fault diagnosis. This study may offer recommendations for enhancing the condition monitoring of rolling bearings under extreme working conditions.

Tooth friction in spur gear transmission. From local to mean tooth friction loss

In a gear transmission, power losses come from a variety of sources, classified as load-dependent and no-load-dependent losses. No-load-dependent losses are the ones that remain constant regardless of the applied load, such as losses due to drag in rolling element bearings, seals, churning and/or windage for gears. Load-dependent losses include friction losses due to sliding and rolling between gear teeth. These losses are particularly important in some gear transmissions because of their direct impact on overall efficiency and thermal behaviour. Consequently, knowledge of the friction coefficient associated to this loss is of major interest for optimising gears (geometry, surface finish, lubrication, etc.). In this article, a new method is proposed for predicting the mechanical efficiency losses associated with the mean coefficient of friction for pairs of cylindrical gears. It is based on a model developed previously to estimate the instantaneous tooth friction along the plane of action in gears. This model takes into account the lubricant shear and the friction on contacting asperities. The proposed mean friction coefficient is deduced from the instantaneous friction coefficient using specific calculations, particularly at the pitch point. To verify the method, the estimated results are compared with those in the literature calculated for gears and operating conditions common on FZG test rigs.

Extended crack propagation in nitrided and carburised steels subject to rolling contact fatigue

To predict the Rolling Contact Fatigue life of components such as rolling element bearings, it is important to understand the failure mechanisms. A rather unusual failure was observed on integrated races into a planetary gear train. The failure of the planet of this gearbox was attributed to the very long propagation, at an unusual depth, of a crack, which probably started at the surface. The present preliminary study aims to gain a better understanding of the factors that can lead to this type of failure. Firstly, a twin-disc machine is used to reproduce the contact conditions identified in the application. A second set of more severe conditions is also tested. A comparison is made between a carburised steel (16NiCrMo13) and a nitrided one (32CrMoV13). Two distinct crack patterns and spalling characteristics between the two materials are observed. Some unusual crack characteristics are observed in the carburised sample for a specific set of contact conditions.

Multi-source domain adaptation using diffusion denoising for bearing fault diagnosis under variable working conditions

Transfer learning of multi-source domain adaptation seems a promising way for fault diagnosis of roller element bearings under variable working conditions. Data imbalance affects the performance of multi-source domain adaptation greatly and is expected to be solved by GAN. However, GAN-based transfer learning diagnosis models suffer pattern collapse and training instability, leading to unsatisfying diagnosis results in practical engineering. This paper proposes a denoising diffusion multi-source domain adaptation model (DDMDA). The proposed model uses diffusion denoising, which has better performance and is simpler to train than GAN, to generate shifted source domains for solving the data imbalance problem. A new noise prediction structure in diffusion denoising named Utrans-net, is constructed to restore the data distribution in the shifted source domain. Also, a multiple-domain discriminator structure is designed to extract features from multiple source domains to solve the issue of variable working conditions. Advanced models are used in this paper to compare with the proposed model for validation. Experimental demonstrations show that the proposed model is superior to the comparison models with satisfying performance.

A core space gradient projection-based continual learning framework for remaining useful life prediction of machinery under variable operating conditions

Recently, continual learning has received particular attention in machinery remaining useful life (RUL) prediction, which enables prognostics networks to gradually improve performance without laborious retraining. Existing studies, however, have the following limitations: 1) The impacts of variable operating conditions are not explicitly considered during continual learning. 2) The continual learnability of prognostics network is limited by the lack of knowledge compression and de-redundancy. To overcome the abovementioned limitations, a novel continual learning framework is proposed for RUL prediction of machinery under variable operating conditions. First, a multi-kernel swarm convolution block is devised to automatically capture degradation features during continual learning without increasing computational consumption, which combines convolutional operations at various scales and focuses attention on appropriate scales based on operating condition information. Then, core space gradient projection is proposed for continual learning of prognostics networks, which mitigates forgetting by guiding gradient descent along the orthogonal direction of the previously input subspace. This approach also ensures the network learnability during continual learning by knowledge compression and de-redundancy to identify core space. The proposed framework is verified using accelerated degradation datasets of rolling element bearings with variable operating conditions. Experimental results show that the proposed framework is superior to some existing continual learning-based prognostics approaches for RUL prediction under variable operating conditions.

Analysis of temperature characteristics of high‐speed train wheelset system under complex operating conditions

AbstractWith the rapid advancements in high‐speed train technology, the importance of ensuring the safety of train operations has become paramount. Bearings, being a critical component of train bogies, have garnered significant attention for their role in maintaining safety standards. Monitoring the temperature of bearings to evaluate their motion state is a common practice in high‐speed trains, emphasizing the need for further research into temperature fluctuations. In this study, a dynamic model is developed for the bearing rotor system of high‐speed trains. By considering the contact points between raceways and rolling elements, the power loss in the bearing is obtained and a transient temperature‐field model of the system is established. The relationship between node temperature and factors such as ambient temperature, train running speed, and load is illustrated, with a detailed presentation of the influence of bearing fault type and size on node temperature. The analysis results reveal that the node temperature increases with higher values corresponding to those quantifiable factors and is most affected by rolling element fault. Additionally, it is observed that the temperature rises rapidly in the initial stage and gradually flattens out over time. The comparative analysis of temperature under different fault conditions shows that the node temperature is most affected by the rolling element fault. Experiments and actual line temperature data are used to verify the validity of the model. The comparison results show that the simulation aligns well with experimental and line data. The transient temperature‐field model of the bearing rotor system in high‐speed trains can effectively simulate and predict the temperature change process of each node of the system. The simulation results hold certain theoretical guiding significance for further research and practical applications in ensuring train operation safety.

Prediction of remaining useful life for rolling bearing based on ISOMAP and multi-head self-attention with gated recurrent unit

The remaining useful life of rolling element bearing affects the reliability and stability of the machine. Accurately predicting the remaining useful life of rolling element bearings is always necessary to make maintenance decisions in practical engineering. However, the remaining useful life prediction methods using traditional machine learning are incompetent for the task of training without labels, which consumes computational cost, financial resources, and labor. Therefore, a gated recurrent unit model based on feature dimensionality reduction and the multi-head self-attention mechanism (MHGRU) is established, and a homology algorithm is proposed to train the model and make the prediction. The isometric feature mapping algorithm and a homology algorithm are used in the model training, which incorporates the advantages of appreciable computational efficiency and preservation of automatic labeling for training in engineering. First, 24 basic features are extracted from the life-cycle vibration signals of rolling element bearings to reconstruct fusion features by the isometric feature mapping algorithm, which aims to reduce the feature dimension and improve computational efficiency. Since the multi-head self-attention mechanism in the MHGRU has the ability to comprehensively highlight the attention coefficient for long-term fusion features at different moments, it gives the gated recurrent unit considerable performance in reducing the computational complexity of extremely long time series and improving the accuracy of prediction results. In addition, two open-source IEEE PHM Challenge and XJTU-SY bearing datasets sampled by the sensor are adopted to assess the prediction performance of the MHGRU with homology algorithm. Finally, some existing remaining useful life prediction approaches of rolling element bearings are used for comparison. The experimental results show that MHGRU is suitable for the remaining useful life prediction of rolling element bearings and is superior to other models.

Encoder signal-based optimized Savitzky-Golay and adaptive spectrum editing for feature extraction of rolling element bearing under low-speed and variable-speed conditions

Feature extraction of rolling element bearings (REB) under variable-speed conditions is always one of the hot and difficult points in the field of fault diagnosis. Based on the encoder signal with the advantages of low noise, and direct correlation with machine dynamics, an optimized Savitzky-Golay and adaptive spectrum editing are proposed for REB feature extraction under low-speed and variable-speed conditions. Firstly, the estimated features of the instantaneous angular speed (IAS) and interference components are studied. Secondly, based on the proposed multipoint mean ratio indicator and parametric decomposition structure, an adaptive SG filter is proposed to remove the speed trend component. Thirdly, an adaptive spectrum editing scheme with no transition band and low computational cost advantages is proposed to detect REB fault based on the combination of the cyclic dislocation scheme, the Gaussian function and the Pearson theory. Simulation and experiments are used to verify the effectiveness of the proposed scheme.

Influence of axial clearance on load distribution in double supported tapered roller bearings of wind turbine main shaft

The operational safety and stability of wind turbines are highly determined by the characteristics of main bearings, which are influenced by the axial clearance. In this paper, for the arrangement type of double-supported tapered roller bearings on the wind turbine main shaft, the actual clearance distribution assumption is established, and a new method of calculating the internal load distribution taking into account the axial clearance is proposed. Firstly, the mathematical relationship between the clearance of tapered roller bearings and ring displacement, contact deformation, and load interval is described. Secondly, the load components of main bearings are calculated by force balance equation and load-displacement condition. Finally, a mathematical model of main bearings including axial clearance is established, and it is numerically solved to obtain the internal load distribution of the bearings. The calculation results of a large megawatt wind turbine example show that: the axial load of both main bearings is negatively correlated with the sum of axial clearances; the number of loaded rolling elements is negatively correlated with the sum of axial clearances; and there exists an optimal value of the sum of axial clearances for main bearings to reach the optimal load carrying state.

Weighted squared envelope dispersion entropy as nonlinear measure for dynamic health monitoring of rotating machineries

In the context of nondestructive testing and evaluation, dispersion entropy (DisE) stands out as a promising dynamic nonlinear health monitoring measure for rotating machineries. However, in high-noise scenarios, transient impulses linked to rotating machinery faults often get submerged under the noise component present in the collected vibration signal. As a result, DisE not only fails to detect the presence of a fault at the earliest stage of inception but also performs poorly in tracking the progression of the incepted fault. Aiming at overcoming the limitations of DisE in dynamic health monitoring of rotating machineries, in this paper, impulses corresponding to a fault is extracted by suppressing the unnecessary noise component by weighting the squared envelope of the collected vibration signal. Due to the application of weighted squared envelope in calculating the DisE, the proposed measure is termed as weighted squared envelope dispersion entropy (WSEDisE). Effectiveness of WSEDisE in dynamic health monitoring of rotating machineries is verified by two different experimental run to failure data collected from rolling element bearings and spur gears. Experimental results show that WSEDisE not only overcomes the weaknesses of original DisE but also demonstrates better performance than conventional entropy-based methods such as permutation entropy (PE) and advanced DisE based method namely multiscale DisE (MDisE).

Clustering-based regularized orthogonal matching pursuit algorithm for rolling element bearing fault diagnosis

Clustering-based regularized orthogonal matching pursuit algorithm for rolling element bearing fault diagnosis

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable Sr Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

A method to develop a high-fidelity gearbox thermal model based on limited temperature measurements

This paper presents a method to create a high-fidelity gearbox thermal model. A hybrid analytical-empirical model is presented, it is based on the thermal network approach and experiments are used to determine some unknown parameters of the model. A test campaign was led on a four stages helical gear unit and consisted of cooling measurements, no-load tests and loaded tests. These experiments were performed under different rotational speeds and loads. The results show that the developed thermal model provides an accurate prediction of the thermal behavior of different components of the gearbox (namely rolling element bearings and oil) by only using few thermal sensors. These results are corroborated by power losses measurements which demonstrate that the model can estimate the gear unit efficiency.

Local damage detection in rolling element bearings based on a single ensemble empirical mode decomposition

A Single Ensemble Empirical Mode Decomposition (SEEMD) is proposed to locate the damage in rolling element bearings. The SEEMD method eliminates the need for adding or subtracting noise repeatedly to process signals, unlike other techniques that rely on multiple ensembles. The SEEMD requires a single sifting process of a modified raw signal to reduce the computation time significantly. The other advantage of the SEEMD method is its success in dealing with non-Gaussian or non-stationary perturbing signals. In SEEMD, a fractional Gaussian noise (FGN) is initially added to the raw signal to emphasize the signal's high frequencies. Then, a convoluted white Gaussian noise is multiplied on the resulting signal, changing its spectral content, which helps in extracting the weak periodic signal. Finally, the obtained signal is decomposed using a single sifting process. The proposed methodology is applied to the raw signals obtained from the mining industry. These signals are difficult to analyze since cyclic impulsive components are obscured by noise and other interference. Based on the results, the proposed method can effectively detect the fault where the signal of interest (SOI) has been extracted with good quality.

Study on internal load distribution of deep groove ball bearings considering raceway surface waviness

Abstract A full circumferential contact mechanics modeling study considering the effect of waviness mesoscopic profile and rolling element contact relationship on bearing load distribution is carried out. In the model, a sinusoidal function is adopted to simulate the profile of the waviness section. According to the contact tangency condition, a model of the contact deformation relationship between a single rolling element and the raceways is established. On this basis, a full circumferential rolling elements contact load distribution model of the bearing is established by introducing the contact deformation of all rolling elements with the raceway surfaces. Focusing on contact deformation of a full circumferential rolling elements with the raceways, this model can conduct more accurate modeling and reflect the load distribution state inside the bearing more realistically. The correctness of the mechanical analysis model proposed in this paper is proved by an average error of 3.38%, which is the comparison result between the mechanical analysis model and the finite element model. Meanwhile, computational efficiency is improved. Further, based on the model, the influence of the waviness parameters on the load distribution inside the bearing under the action of different types of loads is analyzed.

Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art

Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage. Rotating machinery under continuous overload conditions can indeed significantly degrade bearing life and lead to various other issues. To identify issues in rolling element bearings (REB), several techniques and methods are employed. Diagnosing faults in ball bearings while simultaneously estimating the Remaining Useful Life (RUL) of the bearing is a crucial aspect of predictive maintenance. This can be achieved through a combination of signal processing techniques, machine learning methods, and RUL prediction models. The estimation of a bearing Remaining Useful Life (RUL) is of significant importance in predictive maintenance strategies to avoid unexpected failures, reduce downtime, and optimize maintenance costs. This literature review aims to explore the methodologies, techniques, and advancements in predicting the remaining useful life of bearings.