Single-channel de-correlation, separation and correction of train bearing fault sound signals

PDF(4135 KB)

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (13) : 137-146.

ZHAO Xinhang1, LIU Fang1,2, HUANG Mingtao1, ZHU Zihao1, HOU Chaoqiang1, LIU Yongbin1,2

Author information +

History +

Abstract

A single channel de-correlation source separation time-domain interpolation resampling method (SCDBSS-TIR) is proposed for the wheel-track contact noise interference and Doppler aberration problems in track-side acoustic detection system. A single microphone directly opposite the wheelset bearing is used to acquire noisy single-channel observation signals, which are firstly converted into two-channel observation signals using singular spectrum analysis; then multiple time-delay correlation matrix eigenvalue decomposition is used for sound source separation; finally, Doppler aberration correction is performed using the time-domain interpolation resampling method. Simulations and experiments show that the proposed method outperforms the classical determined blind source separation method under the influence of Doppler effect, and also has a good effect on in-band noise cancellation. It is expected to be applied in TADS.

Key words

train bearing / fault diagnosis / TADS / blind source separation / singular spectrum analysis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHAO Xinhang1, LIU Fang1,2, HUANG Mingtao1, ZHU Zihao1, HOU Chaoqiang1, LIU Yongbin1,2. Single-channel de-correlation, separation and correction of train bearing fault sound signals[J]. Journal of Vibration and Shock, 2023, 42(13): 137-146

References

[1] CLINE J E, BILODEAU J R, SMITH R L. Acoustic wayside identification of freight car roller bearing defects[C]//Proceedings of the 1998 ASME/IEEE joint railroad conference. NY, USA: IEEE, 1998. 79-83.
[2] 陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J]. 振动与冲击,2012, 31(15): 82-86.
CHEN Renxiang, TANG Baoping, MA Jinghua.An adaptive noise reduction method for vibration signals based on EEMD[J]. Vibration and Shock, 2012, 31(15): 82-86.
[3] LIU F, SHEN C, HE Q, et al. Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2014, 228(18): 3356-3373.
[4] LI M, LIU Y, ZHI S, et al. Short-time Fourier Transform Using Odd Symmetric Window Function[J]. Journal of Dynamics, Monitoring and Diagnostics, 2021, 1(1): 37–45.
[5] ZHANG S, HE Q, OUYANG K, et al. Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement[J]. Mechanical Systems and Signal Processing, 2018, 100: 224-241.
[6] JUTTEN C, HERAULT J. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture[J]. Signal processing, 1991, 24(1): 1-10.
[7] 胥永刚, 孟志鹏, 赵国亮. 基于双树复小波变换的轴承复合故障诊断研究[J].仪器仪表学报,2014,35(02):447-452.
XU Yonggang, MENG Zhipeng, ZHAO Guoliang. Research on composite fault diagnosis of bearings based on double-tree complex wavelet transform[J]. Journal of Instrumentation, 2014, 35(02): 447-452.
[8] 李志农, 张芬, 肖尧先. 基于CVA-ICA的机械故障源动态盲分离方法[J]. 机械工程学报, 2015, 51(12): 24-29.
LI Zhinong, ZHANG Fen, XIAO Yaoxian. Dynamic blind separation method of mechanical fault sources based on CVA-ICA[J]. Journal of Mechanical Engineering, 2015, 51(12): 24-29.
[9] 孟宗, 蔡龙.基于EEMD子带提取相关机械振动信号单通道盲源分离[J]. 振动与冲击, 2014, 33(20): 40-46+51.
MENG Zong, CAI Long. Single-channel blind source separation of correlated mechanical vibration signals based on EEMD subband extraction[J]. Vibration and Shock, 2014, 33(20): 40-46+51.
[10] 刘战存.多普勒和多普勒效应的起源[J].物理,2003(07): 488-491.
LIU Zhancun. The origin of Doppler and the Doppler effect[J]. Physics, 2003(07): 488-491.
[11] 胥永刚,张志新,马朝永,等.改进奇异谱分解及其在轴承故障诊断中的应用[J].振动工程学报,2019, 32(03): 540-547.
XU Yonggang, ZHANG Zhixin, MA Chaoyong, et al. Improved singular spectrum decomposition and its application in bearing fault diagnosis[J]. Journal of Vibration Engineering, 2019, 32(03): 540-547.
[12] 肖小兵,刘宏立,马子骥.基于奇异谱分析的经验模态分解去噪方法[J].计算机工程与科学,2017, 39(05): 919-924.
XIAO Xiaobing, LIU Hongli, MA Ziji. An empirical modal decomposition denoising method based on singular spectrum analysis[J]. Computer Engineering and Science, 2017, 39(05): 919-924.
[13] 齐子元,米东,徐章隧,等.奇异谱分析在机械设备故障诊断中的应用[J].噪声与振动控制,2008(01): 82-84+88.
QI Ziyuan, MI Dong, XU Zhangtun, et al. Application of singular spectrum analysis in fault diagnosis of mechanical equipment [J]. Noise and Vibration Control, 2008(01): 82-84+88.
[14] ALEXANDROV T, GOLYANDINA N. Automatic extraction and forecast of time series cyclic components within the framework of SSA[C]//Proceedings of the 5th St. Petersburg Workshop on Simulation. St. Petersburg: St. Petersburg State University, 2005. 45-50.
[15] 江涌,章林柯,何琳,等.两次去相关用于振动信号盲分离[J].振动.测试与诊断,2011,31(02):241-245+270.
JIANG Yong, ZHANG Linke, HE Lin, et al. Double decorrelation for blind separation of vibration signals [J]. Vibration. Testing and Diagnosis, 2011, 31(02): 241-245+270.
[16] 于刚,周以齐,张卫芬.一种新的相关机械振源盲分离方法[J]. 振动与冲击,2016, 35(15): 216-221.
YU Gang, ZHOU Yiqi, ZHANG Weifen.A new blind separation method of related mechanical vibration sources[J].Vibration and Shock, 2016, 35(15): 216-221.
[17] TONG L, LIU R. Blind estimation of correlated source signals[C]//1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990. NY, USA: IEEE, 1990. 1-258.
[18] 刘方, 何清波, 沈长青, 等.运动声源多普勒畸变信号的一种时域矫正方法[J]. 声学学报, 2014, 39(02): 185-190.
LIU Fang, HE Qingbo, SHEN Changqing, et al. A time-domain correction method for Doppler distortion signals of motion sound sources[J]. Journal of Acoustics, 2014, 39(02): 185-190.