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Xiaochao Wang
Institute of Rail Transit, Tongji University, Shanghai 201804, PR China

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Journal article
Published: 14 December 2020 in Mechanism and Machine Theory
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Axle box bearings are important parts of rail vehicle running gear. Currently, there is limited research on the coupling modeling of an axle box bearing and rail vehicle. In this paper, a complete method for bearing modeling with multitype defects is proposed. The bearing kinematics with three translation and one rotation degrees of freedom are analyzed and modeled, and defect modeling algorithms for the inner raceway, outer raceway and rollers are proposed. The theoretical rolling tracks of rollers passing through the defect points are analyzed and formulated. By coupling the bearing model to the motion joint of the shaft end and axle box, a closed-loop bearing-vehicle model is established. Model simulations show that the calculated results for normal bearings, different types of bearing defects and the bearing-vehicle coupling model are consistent with the theoretical values. The novel and complete coupling model proposed in this paper has the advantages of having a simple algorithm, high computational efficiency, and accurate simulation of the bearing vibration response under train running conditions, which can provide important guidance for research on the response of an axle box bearing in train running gear. By establishing a bearing-vehicle coupling model that simulates the bearing vibration response, especially when the bearings have faults, the performance of the defective bearing can be better understood and applied to develop diagnostic algorithms for the condition monitoring of axle box bearings in a rail vehicle.

ACS Style

Zhenggang Lu; Xiaochao Wang; Keyu Yue; Juyao Wei; Zhe Yang. Coupling model and vibration simulations of railway vehicles and running gear bearings with multitype defects. Mechanism and Machine Theory 2020, 157, 104215 .

AMA Style

Zhenggang Lu, Xiaochao Wang, Keyu Yue, Juyao Wei, Zhe Yang. Coupling model and vibration simulations of railway vehicles and running gear bearings with multitype defects. Mechanism and Machine Theory. 2020; 157 ():104215.

Chicago/Turabian Style

Zhenggang Lu; Xiaochao Wang; Keyu Yue; Juyao Wei; Zhe Yang. 2020. "Coupling model and vibration simulations of railway vehicles and running gear bearings with multitype defects." Mechanism and Machine Theory 157, no. : 104215.

Journal article
Published: 05 September 2019 in Entropy
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The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconstruction (EFR) and composite multiscale permutation entropy (CMPE). First, a wavelet packet transform (WPT) is applied to decompose the vibration signals into multiple frequency bands. Then, considering that the bearing-localized defects cause the axle-box bearing system to resonate at a high frequency, which will lead to uneven energy distribution of the signal in the frequency domain, the energy factors of each frequency band are calculated by an energy feature extraction algorithm, from which the frequency band with maximum energy factor (which contains abundant fault information) is reconstructed to the time-domain signal. Next, the complexity of the reconstructed signals is calculated by CMPE as fault feature vectors. Finally, the feature vectors are input into a medium Gaussian support vector machine (MG-SVM) for bearing condition classification. The proposed method is validated by a public bearing data set and a wheelset-bearing system test bench data set. The experimental results indicate that the proposed method can effectively extract bearing fault features and provides a new solution for condition monitoring and fault diagnosis of rail vehicle axle-box bearings.

ACS Style

Xiaochao Wang; Zhenggang Lu; Juyao Wei; Yuan Zhang. Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy. Entropy 2019, 21, 865 .

AMA Style

Xiaochao Wang, Zhenggang Lu, Juyao Wei, Yuan Zhang. Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy. Entropy. 2019; 21 (9):865.

Chicago/Turabian Style

Xiaochao Wang; Zhenggang Lu; Juyao Wei; Yuan Zhang. 2019. "Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy." Entropy 21, no. 9: 865.