Surface wear evolution of traction motor bearings in vibration environment of a locomotive during operation

Liu, Yuqing; Chen, Zaigang; Wang, Kaiyun; Zhai, Wanming

doi:10.1007/s11431-021-1939-3

Cited by 24 publications

(6 citation statements)

References 33 publications

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“…The outcomes demonstrate that the wear of the inner ring of the motor bearing is relatively uniform. In addition, the increase in surface wear of the motor will increase internal power and worsen the vibration of the traction motor and its adjacent components in the locomotive [8].…”

Section: Related Workmentioning

confidence: 99%

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Rolling Bearing Life Prediction Technology Based on Feature Screening and LSTM Model

Zhao

2024

IJACSA

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As one of the important components of industrial equipment, the health condition of rolling bearings will directly affect the operational effectiveness of the equipment. Therefore, to ensure equipment safety and reduce maintenance costs, an intelligent rolling bearing life prediction technology is proposed. Firstly, it extracts the fault information of rolling bearings and introduces Fisher score for feature selection. Simultaneously, a variational modal analysis method on the grounds of improved particle swarm optimization is introduced to achieve denoising of rolling bearing signals. Finally, an improved bidirectional long short-term model is introduced to construct a prediction model and achieve the life prediction of rolling bearings. In the performance analysis of the denoising model, the optimal modal component K value of the denoising model was obtained through experimental analysis as 3, and the optimal penalty factor number was 1000. In the time-domain signal analysis of the two models, the proposed model possesses a more excellent decomposition effect on the original signal compared to the comparative model, and the signal denoising ability is improved by 26.35%. In the prediction of rolling bearing life, the proposed model can accurately predict the early and late life of rolling bearings. For example, when the collection time is 100, the actual remaining life is 0.712, and the proposed model is 0.721, which is better than other models. In the comparison of average absolute error, the proposed model is 0.035, which outperforms other models. This indicates that the proposed rolling bearing life prediction model has excellent predictive performance. The research provides essential technical references for the maintenance of industrial machinery and equipment, as well as equipment life monitoring.

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Section: Related Workmentioning

confidence: 99%

“…Sf between c classes, and the i -th feature value of the training sample can be obtained, as shown in Eq. (8).…”

Section: Max( ( )) Min( ( ))mentioning

confidence: 99%

Rolling Bearing Life Prediction Technology Based on Feature Screening and LSTM Model

Zhao

2024

IJACSA

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“…To analyze the effect of rotor eccentricity on the locomotive system, particularly the traction motor, a spatial locomotive-track coupled dynamics model with an eccentric rotor motor is presented in this section based on the coupled dynamics model proposed by Liu et al 15,30 External and internal excitations such as the nonlinear wheel-rail interaction, gear meshing, internal interactions of the motor bearing, CF, RIF, UMP, gravity eccentric torque, and friction torque are comprehensively considered. The mechanical structures and dynamics model of the locomotive subsystem are shown in Figures 1 and 2, respectively.…”

Section: Locomotive-track Spatially Coupled Dynamics Model With An Ec...mentioning

confidence: 99%

Effect of rotor eccentricity on the dynamic performance of a traction motor and its support bearings in a locomotive

Liu

Chen

Xia

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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Rotor eccentricity is a common fault of traction motors during operation. To ensure the stability and safety of the traction motor, it is essential to analyze the effect of rotor eccentricity on the dynamic performance of the traction motor, particularly the motor bearings. This study focuses on the extraction of the fault vibration features of a locomotive in the presence of rotor eccentricity under complex internal and external excitations from the traction power transmissions and wheel-rail interactions. A spatial locomotive-track coupled dynamics model with an eccentric rotor motor is applied in this study, and the detailed mechanical structures of the traction motor and motor bearings are comprehensively considered. The results indicate that rotor eccentricity can deteriorate the vibration conditions of the traction motor and intensify the interactions between the components of the motor bearings. Due to the effect of the time-varying mesh forces, the variation in the loaded region of the non-driving end bearing is more significant than that of the driving end bearing.

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“…Rolling bearings play a crucial role in modern industry [1]. Bearing failures, predominantly attributed to wear [2] and fatigue cracking [3,4], significantly compromise equipment reliability and safety. Such malfunctions may precipitate grave accidents and operational halts.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Yan,

Liao,

Zhang

et al. 2024

Machines

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In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.

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Surface wear evolution of traction motor bearings in vibration environment of a locomotive during operation

Cited by 24 publications

References 33 publications

Rolling Bearing Life Prediction Technology Based on Feature Screening and LSTM Model

Rolling Bearing Life Prediction Technology Based on Feature Screening and LSTM Model

Effect of rotor eccentricity on the dynamic performance of a traction motor and its support bearings in a locomotive

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

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