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Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement

机译:基于时空空间滤波重排的路边声学诊断多方位弱缺陷检测

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摘要

The wayside Acoustic Defective Bearing Detector (ADBD) system plays an important role in ensuring the safety of railway transportation. However, Doppler distortion and multi-bearing source aliasing in the acquired acoustic bearing signals significantly decrease the accuracy of bearing diagnosis. Traditional multisource separation schemes using time-frequency filters constructed by a single microphone signal always show poor performance on weak signal separation. Inspired by an assumption that the spatial location of different sources is different, this paper proposes a novel time-varying spatial filtering rearrangement (TSFR) scheme based on a microphone array to overcome current difficulties. In the scheme, a zero-angle spatial filter and peak searching are proposed to obtain the time-centers of corresponding sources. Based on these time-centers, several time-varying spatial filters are designed to extract different source signals. Then interpolation and rearrangement are used to correct the Doppler distortion and reconstruct the corresponding separated signals. Finally, the train bearing fault diagnosis is implemented by analyzing the envelope spectrum of the corrected signals. Because the time-varying spatial filter construction is only dependent on the source location and has little relationship with the signal energy, the proposed TSFR scheme has significant advantages in weak signal separation and diagnosis in comparison with traditional ones. With the verifications by both simulation and experiment cases, the proposed array-based TSFR scheme shows a good performance on multiple fault source separation and is expected to be used in the ADBD system.
机译:路边的声学缺陷轴承检测器(ADBD)系统在确保铁路运输安全方面起着重要作用。然而,所获取的声学方位信号中的多普勒失真和多方位源混叠大大降低了方位诊断的准确性。传统的使用单个麦克风信号构成的时频滤波器的多源分离方案在弱信号分离方面始终显示出较差的性能。受到不同源空间位置不同的假设的启发,本文提出了一种基于麦克风阵列的新型时变空间滤波重排(TSFR)方案,以克服当前的困难。在该方案中,提出了一种零角度空间滤波器和峰值搜索方法,以获得相应源的时间中心。基于这些时间中心,设计了多个时变空间滤波器以提取不同的源信号。然后使用插值和重排来校正多普勒失真并重建相应的分离信号。最后,通过分析校正信号的包络谱来实现列车轴承故障诊断。由于时变空间滤波器的构造仅取决于信号源的位置,并且与信号能量的关系很小,因此与传统方法相比,所提出的TSFR方案在信号分离和诊断能力较弱方面具有明显优势。通过仿真和实验案例的验证,所提出的基于阵列的TSFR方案在多个故障源分离方面显示出良好的性能,并有望在ADBD系统中使用。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2018年第1期|224-241|共18页
  • 作者单位

    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, PR China;

    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, PR China;

    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, PR China;

    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, PR China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Train bearing; Fault diagnosis; Doppler distortion; Multisource separation; Microphone array;

    机译:火车轴承;故障诊断;多普勒失真;多源分离;麦克风阵列;

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