Observing early stage rail axle bearing damage

Symonds, Nicola; Corni, Ilaria; Wood, R.J.K.; Wasenczuk, Adam; Vincent, David

doi:10.1016/j.engfailanal.2015.02.008

Cited by 33 publications

(21 citation statements)

References 8 publications

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“…However, the Raman spectrum peak strength of the severe twin samples is significantly higher than that of the former, indicating that the severe oxidation of the contact area of the severe twin samples means that more oxides were generated. Raman spectral analysis shows that the oxide generated by friction is mainly Fe 2 O 3 , which is consistent with the conclusion in the literature (Symonds et al, 2015). According to studies in the literature (Sullivan and Athwal, 1983), the predominant oxides are Fe 2 O 3 for contact temperature of friction pairs up to 200 • C, and Fe 3 O 4 for temperatures between 200 and 570 • C. This shows that the maximum temperature of the friction interface in this test was about 200…”

Section: Experimental Methodssupporting

confidence: 91%

Influence of Twin Carbide Structure on Friction and Wear Properties of G95Cr18 Stainless Bearing Steel

Guo

Lei

et al. 2019

Front. Mater.

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G95Cr18 stainless bearing steel is used to manufacture rolling bearings widely used in aviation and navigation fields. Twin carbide structure often appears in G95Cr18 stainless bearing steel structure and affects its material properties. This study adopts ball-block friction pairs to qualitatively discuss the influence of twin carbide structure of different degrees on the friction and wear performance of G95Cr18 stainless bearing steel under dry sliding conditions at room temperature and atmospheric environment. The results show that a slight twin carbide structure has only a little influence on the friction coefficient but can reduce the wear rate slightly, while a severe twin carbide structure can significantly increase the friction coefficient and wear rate. Thus, the existence of an optimal value for twin carbide structure was confirmed. The wear mechanism is mainly oxidation wear and abrasive wear, and the oxide produced by oxidation reaction during friction consists mainly of Fe 2 O 3 .

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Section: Experimental Methodssupporting

confidence: 91%

Influence of Twin Carbide Structure on Friction and Wear Properties of G95Cr18 Stainless Bearing Steel

Guo

Lei

et al. 2019

Front. Mater.

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“…Axle-box bearings, as the key component of the high-speed train, can have a significant impact on the security, stability and sustainability of railway vehicles [ 1 ]. If an axle-box bearing failure is not detected promptly, it may cause severe delays or even dangerous derailments, implicating human life prejudice and significant costs for railway managers and operators.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Luo

Lin

Peng³

et al. 2020

Sensors

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Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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“…From the start of service to performance failure, the state degradation history of high-speed railway bearings is a gradual process that contains the continuous evolutionary trajectories of performance and reliability. Xu et al [10][11][12] extracted the features of the vibration signal of railway bearings accurately and effectively and made a quantitative evaluation of the bearing fault and reliability problems, which provided a theoretical basis for the quality control and reliability design of rolling bearings. Gallego-Calderon et al [13] investigated the impact on the reliability of different configurations of planet bearings and indicated a relation between the reliability index and the bearing dynamic rating.…”

Section: Introductionmentioning

confidence: 99%

Dynamic prediction of the performance reliability of high-speed railway bearings

Liang

Xia

Chang³

2019

J Braz. Soc. Mech. Sci. Eng.

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Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs.

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Observing early stage rail axle bearing damage

Cited by 33 publications

References 8 publications

Influence of Twin Carbide Structure on Friction and Wear Properties of G95Cr18 Stainless Bearing Steel

Influence of Twin Carbide Structure on Friction and Wear Properties of G95Cr18 Stainless Bearing Steel

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Dynamic prediction of the performance reliability of high-speed railway bearings

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