The Doppler Effect based acoustic source separation for a wayside train bearing monitoring system
The Doppler Effect based acoustic source separation for a wayside train bearing monitoring system
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DOI：
10.1016/j.jsv.2015.09.038
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发表时间：
2016-01
影响因子：
4.7
通讯作者：
Haibin Zhang;Shangbin Zhang;Qingbo He;Fanrang Kong
中科院分区：
文献类型：
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作者：
Haibin Zhang;Shangbin Zhang;Qingbo He;Fanrang Kong
列车轴承轨旁声学状态监测和故障诊断依赖于采集的声信号，声信号由来自不同列车轴承的具有明显多普勒失真的混合信号和背景噪声组成。本研究提出了一种新的方案，以克服困难，特别是多源问题的路旁声学诊断系统。在该方法中，时频数据融合（TFDF）策略被应用到削弱海森堡的不确定性限制的信号的时频分布（TFD）的高分辨率。由于多普勒效应，即使在相同的频率下，来自不同方位的信号也具有不同的时间中心。提出了一种多普勒特征匹配搜索算法（DFMS）来定位TFD谱图中不同方位的时间中心。利用确定的时间中心，设计了具有阈值的时频滤波器（TFF），以在时频域中分离声信号。然后对每个声源进行逆短时傅里叶变换（ISTFT），对信号进行恢复和滤波。随后，利用动态复位方法来消除多普勒效应。最后，利用传统的频谱分析技术对重采样数据进行处理，可以实现对列车轴承故障的准确诊断。仿真和实验结果验证了该方法的有效性。结果表明，即使多个轴承产生多源声信号，该方法也能有效地检测和诊断多个轴承故障。
Wayside acoustic condition monitoring and fault diagnosis for train bearings depend on acquired acoustic signals, which consist of mixed signals from different train bearings with obvious Doppler distortion as well as background noises. This study proposes a novel scheme to overcome the difficulties, especially the multi-source problem in wayside acoustic diagnosis system. In the method, a time–frequency data fusion (TFDF) strategy is applied to weaken the Heisenberg׳s uncertainty limit for a signal׳s time–frequency distribution (TFD) of high resolution. Due to the Doppler Effect, the signals from different bearings have different time centers even with the same frequency. A Doppler feature matching search (DFMS) algorithm is then put forward to locate the time centers of different bearings in the TFD spectrogram. With the determined time centers, time–frequency filters (TFF) are designed with thresholds to separate the acoustic signals in the time–frequency domain. Then the inverse STFT (ISTFT) is taken and the signals are recovered and filtered aiming at each sound source. Subsequently, a dynamical resampling method is utilized to remove the Doppler Effect. Finally, accurate diagnosis for train bearing faults can be achieved by applying conventional spectrum analysis techniques to the resampled data. The performance of the proposed method is verified by both simulated and experimental cases. It shows that it is effective to detect and diagnose multiple defective bearings even though they produce multi-source acoustic signals.