Compound Fault Diagnosis Method Guided by Variational Mode Decomposition for Wheelsets and Bearings

YI Cai; LIN Jianhui; WANG Hao; LIAO Xiaokang; WU Wenyi; RAN Le

doi:10.3969/j.issn.0258-2724.20211088

Volume 59 Issue 1

Jan. 2024

Turn off MathJax

Article Contents

Abstract

References

Journal of Southwest Jiaotong University > 2024 > 59(1): 151-159.

YI Cai, LIN Jianhui, WANG Hao, LIAO Xiaokang, WU Wenyi, RAN Le. Compound Fault Diagnosis Method Guided by Variational Mode Decomposition for Wheelsets and Bearings[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 151-159. doi: 10.3969/j.issn.0258-2724.20211088

Citation:

YI Cai, LIN Jianhui, WANG Hao, LIAO Xiaokang, WU Wenyi, RAN Le. Compound Fault Diagnosis Method Guided by Variational Mode Decomposition for Wheelsets and Bearings[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 151-159. doi: 10.3969/j.issn.0258-2724.20211088

Citation:

PDF( 5309 KB)

Compound Fault Diagnosis Method Guided by Variational Mode Decomposition for Wheelsets and Bearings

doi: 10.3969/j.issn.0258-2724.20211088

YI Cai^1
,,
LIN Jianhui^{1
,
,},
WANG Hao¹,
LIAO Xiaokang²,
WU Wenyi¹,
RAN Le¹

1.
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China
2.
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received Date: 29 Dec 2021
Rev Recd Date: 20 May 2022

Available Online: 18 Jan 2023

Publish Date: 27 May 2022

Abstract

Abstract

A multi-fault feature extraction and matching method guided by variational mode decomposition (VMD) was proposed to address the difficulty in identifying and diagnosing composite faults in train wheelset bearing systems. Firstly, in order to avoid the pre-defined mode number relying on prior knowledge during operation and thus affecting the diagnosis results, the original axle-box vibration data are directly decomposed by VMD step by step, and the number of modes is 2–N. Secondly, the VMD intrinsic mode functions (VIMF) obtained by VMD are calculated to extract the VIMF with the largest correlation kurtosis; then, the determined VIMF is analyzed by square envelope analysis to extract the fault feature frequency. Finally, the proposed method is compared with the fast spectral Kurtogram method and the correlation Kurtogram method. The analysis of simulation signals and experimental data shows that the proposed method can completely avoids the problem of selecting the key parameter K in the VMD model, and can accurately and effectively extract the fault characteristics of wheelsets and bearings, respectively. Compared with the fast spectral Kurtogram method and the correlation Kurtogram method the proposed method can diagnose compound faults effectively, and the obtained fault feature harmonic components are more advantageous in quantity and signal-to-noise ratio.
- variational mode decomposition,
- compound fault diagnosis,
- wheelset bearing,
- correlation kurtosis,
- square envelope

FullText(HTML)

References(14)

References

[1]	刘志亮,潘登,左明健,等. 轨道车辆故障诊断研究进展[J]. 机械工程学报,2016,52(14): 134-146. doi: 10.3901/JME.2016.14.134 LIU Zhiliang, PAN Deng, ZUO Mingjian, et al. A review on fault diagnosis for rail vehicles[J]. Journal of Mechanical Engineering, 2016, 52(14): 134-146. doi: 10.3901/JME.2016.14.134
[2]	赵聪聪,刘玉梅,赵颖慧,等. 基于物元-阴性选择算法的轴箱轴承故障检测[J]. 西南交通大学学报,2021,56(5): 973-980. ZHAO Congcong, LIU Yumei, ZHAO Yinghui, et al. Fault detection of axle box bearing based on matter-element and negative selection algorithm[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 973-980.
[3]	刘国云,曾京,罗仁,等. 轴箱轴承缺陷状态下的高速车辆振动特性分析[J]. 振动与冲击,2016,35(9): 37-42,51. doi: 10.13465/j.cnki.jvs.2016.09.007 LIU Guoyun, ZENG Jing, LUO Ren, et al. Vibration performance of high-speed vehicles with axle box bearing defects[J]. Journal of Vibration and Shock, 2016, 35(9): 37-42,51. doi: 10.13465/j.cnki.jvs.2016.09.007
[4]	LIU Z C, YANG S P, LIU Y Q, et al. Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 154: 107511.1-107511.21.
[5]	XING Z, YI C, LIN J H, et al. Multi-component fault diagnosis of wheelset-bearing using shift-invariant impulsive dictionary matching pursuit and sparrow search algorithm[J]. Measurement, 2021, 178: 109375.1-109375.17.
[6]	WANG D, ZHAO Y, YI C, et al. Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2018, 101: 292-308. doi: 10.1016/j.ymssp.2017.08.038
[7]	DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. doi: 10.1109/TSP.2013.2288675
[8]	黄衍,林建辉,刘泽潮,等. 基于自适应VMD的高速列车轴箱轴承故障诊断[J]. 振动与冲击,2021,40(3): 240-245. doi: 10.13465/j.cnki.jvs.2021.03.032 HUANG Yan, LIN Jianhui, LIU Zechao, et al. Fault diagnosis of axle box bearing of high-speed train based on adaptive VMD[J]. Journal of Vibration and Shock, 2021, 40(3): 240-245. doi: 10.13465/j.cnki.jvs.2021.03.032
[9]	YI C, LI Y Q, HUO X M, et al. A promising new tool for fault diagnosis of railway wheelset bearings: SSO-based Kurtogram[J]. ISA Transactions, 2021, 128: 498-512.
[10]	MIAO Y H, ZHAO M, LIN J. Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification[J]. Measurement Science and Technology, 2017, 28(12): 125001.1-125001.14.
[11]	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 and Vibration, 2007, 308(1/2): 246-267.
[12]	WANG D. Spectral L2/L1 norm: a new perspective for spectral kurtosis for characterizing non-stationary signals[J]. Mechanical Systems and Signal Processing, 2018, 104: 290-293. doi: 10.1016/j.ymssp.2017.11.013
[13]	MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33: 237-255. doi: 10.1016/j.ymssp.2012.06.010
[14]	夏茂森,江玲玲. 变分模态分解模型中关键参数K的辨识研究: 基于加权最大信息系数法[J]. 统计与信息论坛,2021,36(2): 23-35. XIA Maosen, JIANG Lingling. Research on identification of key parameter K in variational mode decomposition model: based on weighted maximum information coefficient method[J]. Journal of Statistics and Information, 2021, 36(2): 23-35.