Fault Diagnosis of Train Wheelset Bearings Based on Fast Average Kurtogram under Variable Speed Conditions

被引：0
|
作者：
Liu W. [1 ]
Yang S. [1 ]
Liu Z. [1 ]
Liu Y. [2 ]
Gu X. [1 ]
机构：
[1] State Key Laboratory of Meehanieal Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang
[2] School of Meehanieal Engineering, Shijiazhuang Tiedao University, Shijiazhuang
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2024年 / 46卷 / 05期
关键词：
average kurtosis; fault diagnosis; iterative generalized demodulation; variable speed; wheelset bearing;
D O I：
10.3969/j.issn.1001-8360.2024.05.005
中图分类号：
学科分类号：
摘要：
Due to the interference of wheel-rail excitation, wheel-set bearing fault characteristics are easily submerged by background noise. The time-varying fault characteristics in variable speed mode further aggravate the difficulty of extraction. For this purpose, a fault feature extraction method for train wheelset bearings was proposed based on the fast spectral average kurtogram. By dividing spectrum into several sub-bands according to the spectral trend, and using the Meyer wavelet to construct a band-pass filter, the average kurtosis of each narrowband filtered signal was calculated and a fast spectral average kurtogram was constructed. Based on the selection of the optimal frequency band for demodulation to eliminate the influence of background noise, the iterative generalized demodulation of the filtered signal was carried out by phase function to solve the spectrum ambiguity caused by time-varying fault impact intervals. The simulation and experimental analysis results show that the proposed method effectively overcomes the interference of strong background noise and time-varying fault features, and provides a new solution strategy for wheel-set bearing fault feature extraction under variable speed conditions. © 2024 Science Press. All rights reserved.
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页码：38 / 47
页数：9
相关论文
共 29 条
  • [1] LI Yongjian, SONG Hao, LIU Jihua, Et al., A Study on Fault Diagnosis Method for Train Axle Box Bearing Based on Modified Multiscale Permutation Entropy [J], Journal of the China Railway Society, 42, 1, pp. 33-39, (2020)
  • [2] GU Xiaohui, YANG Shaopu, LIU Yongqiang, Et al., Fault Diagnosis of Wheel-set Bearing Based on Negentropy and Multi-objective Optimization [J], Journal of Dynamics and Control, 18, 3, pp. 93-99, (2020)
  • [3] SHEN Changqing, WANG Xu, WANG Dong, Et al., Multi-scale Convolution Intra-class Transfer Learning for Train Bearing Fault Diagnosis [J], Journal of Traffic and Transportation Engineering, 20, 5, pp. 151-164, (2020)
  • [4] LIU Dongdong, CHENG Weidong, WEN Weigang, Introduced Energy Factor to Generalized Demodulation and Its Configuration [J], Journal of Vibration Engineering, 33, 1, pp. 213-218, (2020)
  • [5] DENG Feiyue, LIU Pengfei, CHEN Enli, Et al., Multiple Fault Diagnosis of Train Wheelset Bearing Based on Adaptive Frequency Window Empirical Wavelet Transform [J], Journal of the China Railway Society, 41, 5, pp. 55-63, (2019)
  • [6] ZHAO Ming, LIN Jing, Adaptive Extraction and State Evaluation of Mechanical Dynamic Information under Variable Speed [J], Journal of Mechanical Engineering, 51, 8, (2015)
  • [7] ANTONI J., Fast Computation of the Kurtogram for the Detection of Transient Faults [J], Mechanical Systems and Signal Processing, 21, 1, pp. 108-124, (2007)
  • [8] ZHANG Kun, XU Yonggang, MA Chaoyong, Et al., Empirical Fast Kurtogram and Its Application in Rolling Bearing Fault Diagnosis[J], Journal of Vibration Engineering, 33, 3, pp. 636-642, (2020)
  • [9] BARSZCZ T, JABLO'NSKI A., A Novel Method for the Optimal Band Selection for Vibration Signal Demodulation and Comparison with the Kurtogram, Mechanical Systems and Signal Processing, 25, 1, pp. 431-451, (2011)
  • [10] BOZCHALOOI I S, LIANG M., A Smoothness Index-guided Approach to Wavelet Parameter Selection in Signal De-Noising and Fault Detection[J], Journal of Sound Vibration, 308, 1, pp. 246-267, (2007)
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