Detection of Weak Fault Signals for EMU Bearings Under Strong Noise

被引：0

作者：

Sun X.-W. ^{[1
]}

Ji A.-M. ^{[1
]}

Chen X.-H. ^{[1
]}

Lin X.-H. ^{[2
]}

Xu X. ^{[2
]}

机构：

[1] School of Mechanical and Electrical Engineering, Hohai University, Changzhou

[2] CSR Qishuyan Locomotive and Vehicle Technology Research Institute Co., Ltd., Changzhou

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2021年 / 49卷 / 11期

关键词：

Ant colony algorithm; Gearbox bearings; Periodic potential vibration resonance; Wavelet de-noising; Weak fault signal;

D O I：

10.12263/DZXB.20201086

中图分类号：

学科分类号：

摘要：

In the high-speed motion of the motor car, the bearing of the gearbox is prone to cracks, pitting and other failures. In order to detect the weak fault frequency component in the early stage of fault occurrence, a bearing fault diagnosis method based on wavelet de-noising preprocessing and periodic potential vibration resonance feature-enhancing is proposed. Wavelet packet is used to extract the natural resonance frequency band of the bearing, reconstruct the extracted signal, filter out the interference of the strong noise, and then input the signal into the periodic potential vibration resonance system for enhancing the fault characteristics. Meanwhile, an optimization model considering the amplitude of the high-frequency excitation signal in the vibration excitation system is established, and the ant colony algorithm is used to adaptively optimize the parameters. After obtaining the output signal, it is converted into frequency domain analysis to detect early failure of the bearing. The example analysis shows that the error of the proposed method is reduced to 0.3% compared with the result of stochastic resonance. © 2021, Chinese Institute of Electronics. All right reserved.

引用

页码：2217 / 2224

页数：7

共 20 条

[1] Ji Hongquan, Xiao He, Exponential smoothing reconstruction approach for incipient fault isolation, Industrial & Engineering Chemistry Research, 57, 18, pp. 6353-6363, (2018)
[2] Ji Hongquan, Et al., Incipient fault detection with smoothing techniques in statistical process monitoring, Control Engineering Practice, 62, pp. 6211-6221, (2017)
[3] HU Shijun, LIU Chao, Enhanced stochastic resonance method with periodic potential and its application in mechanical fault characteristic extraction, Measurement & Control Technology, 37, 12, pp. 89-93, (2018)
[4] Leng Yonggang, Wang Taiyong, Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy Noise, Acta Physica Sinica, 52, 10, pp. 2432-2437, (2003)
[5] Xie Youhao, Liu Xiaole, Et al., Improved frequency-shifted and re-scaling stochastic resonance for gear fault diagnosis, Transactions of the Chinese Society of Agricultural Engineering, 32, 8, pp. 70-76, (2016)
[6] Wang Linze, Zhao Wenli, Chen Xuan, Theoretical analysis and experimental study of piecewise linear model based on stochastic resonance principle, Acta Physica Sinica, 61, 16, pp. 50-56, (2012)
[7] Yang Jianhua, Han Shuai, Et al., Empirical mode decomposition of weak fault characteristic signal of rolling bearing under strong noise background, Journal of Vibration Engineering, 33, 3, pp. 582-589, (2020)
[8] Liang Bo, Wang Zhihai, Zhang fangwei, Application of SRR based on wavelet packet and integral compensation in bearing fault detection, Industrial Instrumentation & Automation, 3, pp. 8-14, (2020)
[9] (2019)
[10] Xue pingren, Et al., Application of continuous potential function stochastic resonance in early fault diagnosis of rolling bearings, Measurement and Control, 53, 5-6, pp. 767-777, (2020)

← 1 2 →