列车轴承故障声音信号单通道去相关分离矫正

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摘要道旁声学监测信号具有多声源、陡畸变和强噪声的特点，这给基于声音信号的列车轴承故障诊断带来了阻碍。本文针对TADS系统轮轨接触噪声干扰和多普勒畸变问题，提出了一种单通道去相关声源分离矫正方法（SCDBSS-TIR）。采用单个麦克风正对轮对轴承采集含噪单通道观测信号，首先利用奇异谱分析转换为双通道观测信号；然后利用多个时延相关矩阵特征值分解进行声源分离；最后，利用时域插值拟合矫正方法进行多普勒畸变矫正。采用单麦克风-双声源圆周运动模型开展了仿真和实验验证，仿真和实验表明，在多普勒效应影响下，所提方法的分离效果优于经典的正定盲源分离方法，同时对带内噪声的消除效果良好，所提方法有望在TADS中得到应用。

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	赵新航1
	刘方1
	2
	黄明涛1
	朱子豪1
	侯超强1
	刘永斌1
	2

关键词 ：列车轴承, 故障诊断, TADS, 盲源分离, 奇异谱分析

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

收稿日期: 2022-03-03 出版日期: 2023-07-15

引用本文:

赵新航1，刘方1,2，黄明涛1，朱子豪1，侯超强1，刘永斌1,2. 列车轴承故障声音信号单通道去相关分离矫正[J]. 振动与冲击, 2023, 42(13): 137-146.
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. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(13): 137-146.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I13/137

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