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Quantitative evaluation on the performance and feature enhancement of stochastic resonance for bearing fault diagnosis

机译:用于轴承故障诊断的随机共振性能和特征增强的定量评估

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摘要

Stochastic resonance (SR) has been widely applied in the field of weak signal detection by virtue of its characteristic of utilizing noise to amplify useful signal instead of eliminating noise in nonlinear dynamical systems. How to quantitatively evaluate the performance of SR, including the enhancement effect and the degree of waveform distortion, and how to accurately extract signal amplitude have become two important issues in the research on SR. In this paper, the signal-to-noise ratio (SNR) of the main component to the residual in the SR output is constructed to quantitatively measure the enhancement effect of the SR method. And two indices are constructed to quantitatively measure the degree of waveform distortion of the SR output, including the correlation coefficient between the main component in the SR output and the original signal, and the zero-crossing ratio. These quantitative indices are combined to provide a comprehensive quantitative index for adaptive parameter selection of the SR method, and eventually the adaptive SR method can be effective in enhancing the weak component hidden in the original signal. Fast Fourier Transform and Fourier Transform (FFT+FT) spectrum correction technology can extract the signal amplitude from the original signal and effectively reduce the difficulty of extracting signal amplitude from the distorted resonance output The application in vibration analysis for bearing fault diagnosis verifies that the proposed quantitative evaluation method for adaptive SR can effectively detect weak fault feature of the vibration signal during the incipient stage of bearing fault.
机译:随机共振(SR)由于其利用噪声来放大有用信号而不是消除非线性动力系统中的噪声的特性而被广泛应用于弱信号检测领域。如何定量评估SR的性能,包括增强效果和波形失真程度,以及如何准确提取信号幅度已成为SR研究中的两个重要问题。在本文中,构建了SR输出中主要成分与残留之间的信噪比(SNR),以定量测量SR方法的增强效果。构造了两个指标来定量测量SR输出的波形失真程度,包括SR输出中的主要成分与原始信号之间的相关系数以及过零率。这些量化指标组合在一起,为SR方法的自适应参数选择提供了全面的量化指标,最终,自适应SR方法可以有效地增强原始信号中隐藏的弱分量。快速傅里叶变换和傅里叶变换(FFT + FT)频谱校正技术可以从原始信号中提取信号幅度,并有效降低了从失真的共振输出中提取信号幅度的难度。在振动分析中的轴承故障诊断应用证明了该建议自适应SR的定量评估方法可以有效地检测轴承故障初期的振动信号弱故障特征。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2016年第12期|108-125|共18页
  • 作者单位

    State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, China,Xi'an Shiyou University, 710065 Xi'an, China;

    Yanshan University, 066004 Hebei, China;

    State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, China;

    State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, China,The State Key Laboratory for Manufacturing Systems Engineering, Xi'an 710049, PR China;

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

    Stochastic resonance; Fault diagnosis; Quantitative evaluation; Adaptive;

    机译:随机共振;故障诊断;定量评估;适应性强;

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