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首页> 外文期刊>Mechanical systems and signal processing >Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram
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Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram

机译：基于多尺度分数阶熵信息图的重复瞬态提取在机械故障诊断中的应用

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摘要

The presence of repetitive transients in vibration signals is a typical symptom of local faults of rotating machinery. Infogram was developed to extract the repetitive transients from vibration signals based on Shannon entropy. Unfortunately, the Shannon entropy is maximized for random processes and unable to quantify the repetitive transients buried in heavy random noise. In addition, the vibration signals always contain multiple intrinsic oscillatory modes due to interaction and coupling effects between machine components. Under this circumstance, high values of Shannon entropy appear in several frequency bands or high value of Shannon entropy doesn't appear in the optimal frequency band, and the infogram becomes difficult to interpret. Thus, it also becomes difficult to select the optimal frequency band for extracting the repetitive transients from the whole frequency bands. To solve these problems, multiscale fractional order entropy (MSFE) infogram is proposed in this paper. With the help of MSFE infogram, the complexity and nonlinear signatures of the vibration signals can be evaluated by quantifying spectral entropy over a range of scales in fractional domain. Moreover, the similarity tolerance of MSFE infogram is helpful for assessing the regularity of signals. A simulation and two experiments concerning a locomotive bearing and a wind turbine gear are used to validate the MSFE infogram. The results demonstrate that the MSFE infogram is more robust to the heavy noise than infogram and the high value is able to only appear in the optimal frequency band for the repetitive transient extraction.
机译：振动信号中存在重复性瞬变是旋转机械局部故障的典型症状。开发了Infogram，以基于Shannon熵从振动信号中提取重复的瞬态。不幸的是，对于随机过程，Shannon熵被最大化，并且无法量化隐藏在重随机噪声中的重复瞬态。此外，由于机器组件之间的相互作用和耦合作用，振动信号始终包含多个固有振荡模式。在这种情况下，香农熵的高值出现在多个频带中，或者香农熵的高值没有出现在最佳频带中，信息报变得难以解释。因此，选择最佳频带以从整个频带中提取重复瞬变也变得困难。为了解决这些问题，本文提出了多尺度分数阶熵（MSFE）信息报。借助MSFE信息图，可以通过量化分数域中一系列尺度上的频谱熵来评估振动信号的复杂性和非线性特征。此外，MSFE信息报的相似性容忍度有助于评估信号的规律性。关于机车轴承和风力涡轮机齿轮的仿真和两个实验用于验证MSFE信息报。结果表明，MSFE信息报文对重噪声的抵抗力强于信息报文，并且高值只能出现在针对重复瞬态提取的最佳频带中。

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  • 来源
    《Mechanical systems and signal processing》 |2018年第15期|312-326|共15页
  • 作者

    Xuefang Xu; Zijian Qiao; Yaguo Lei;

  • 作者单位

    Key Laboratory of Education Ministry for Modem Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    Key Laboratory of Education Ministry for Modem Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    Key Laboratory of Education Ministry for Modem Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Multiscale fractional order entropy; Infogram; Repetitive transients; Fault diagnosis; Rotating machinery;

    机译：多尺度分数阶熵;信息报;重复瞬变;故障诊断;旋转机械;

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