Further understanding of rolling contact fatigue in rolling element bearings - A review

Laithy, Mostafa El; Wang, Ling; Harvey, Terry J.; Vierneusel, Bernd; Correns, Martin; Blass, Toni

doi:10.1016/j.triboint.2019.105849

Cited by 115 publications

(64 citation statements)

References 140 publications

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“…When there is a defect point on the outer surface of a rolling bearing, the rolling bearing passes the defect point during operation, which will generate a periodic pulse force to the system. Generally, because the outer ring is fixed, the pulse intensity of each cycle is considered as constant, and the periodic pulse signal presents the law of periodic equal amplitude vibration changes [35]. This section simulates the incipient failure of the rolling bearing outer ring.…”

Section: The Rolling Bearing Outer Ring Fault Simulation Resultsmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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Section: The Rolling Bearing Outer Ring Fault Simulation Resultsmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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“…dark etching regions (DERs) and white etching bands (WEBs), are directly associated with the accumulation of plasticity which in turn related to the applied alternating stress range; the evolution of such Mises stress profile also helps to retard the development of visible microstructural alterations. Recent studies [34,36] have shown that microstructural alterations are governed by the maximum distortion energy yield criteria during multiaxial RCF loadings where subsurface plasticity is governed by equivalent von Mises stresses. Inclusion of deep zone residual stresses with von Mises stress profile can better explain the formation mechanism of such microstructural alterations.…”

Section: Resultsmentioning

confidence: 99%

“…Earlier studies [32,33] have employed micro-indentation experiments on RCF damage evaluation and reported a gradual increase in material hardness with the linear accumulation of plastic strains. However, they were unable to evaluate the very localised damage of the RCF damaged zone due to the limitation of micro-indenter that only gives an average hardness over the grain [34]. Current research work overcomes that problem with the use of a Berkovich nanoindenter.…”

Section: Introductionmentioning

confidence: 99%

A 3D Finite Element Model of Rolling Contact Fatigue for Evolved Material Response and Residual Stress Estimation

et al. 2020

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Rolling bearing elements develop structural changes during rolling contact fatigue (RCF) along with the non-proportional stress histories, evolved residual stresses and extensive work hardening. Considerable work has been reported in the past few decades to model bearing material hardening response under RCF; however, they are mainly based on torsion testing or uniaxial compression testing data. An effort has been made here to model the RCF loading on a standard AISI 52100 bearing steel with the help of a 3D Finite Element Model (FEM) which employs a semi-empirical approach to mimic the material hardening response evolved during cyclic loadings. Standard bearing balls were tested in a rotary tribometer where pure rolling cycles were simulated in a 4-ball configuration. The localised material properties were derived from post-experimental subsurface analysis with the help of nanoindentation in conjunction with the expanding cavity model. These constitutive properties were used as input cyclic hardening parameters for FEM. Simulation results have revealed that the simplistic power-law hardening model based on monotonic compression test underpredicts the residual generation, whereas the semi-empirical approach employed in current study corroborated well with the experimental findings from current research work as well as literature cited. The presence of high compressive residual stresses, evolved over millions of RCF cycles, showed a significant reduction of maximum Mises stress, predicting significant improvement in fatigue life. Moreover, the predicted evolved flow stresses are comparable with the progression of subsurface structural changes and be extended to develop numerical models for microstructural alterations. Graphic Abstract

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“…With the vigorous development of the machinery industry, rolling bearings have become an important component of rotating machinery and are widely used in generators, gas engines and other kinds of rotating machinery [ 1 , 2 ]. The local fault of rolling bearing will directly affect the normal operation of the whole of the mechanical equipment, so it is important to explore a new fault diagnosis technique.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

Yan

Jia

2021

Entropy

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The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

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Further understanding of rolling contact fatigue in rolling element bearings - A review

Cited by 115 publications

References 140 publications

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

A 3D Finite Element Model of Rolling Contact Fatigue for Evolved Material Response and Residual Stress Estimation

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

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