基于经验小波变换与谱峭度的船舶轴系故障特征提取方法

Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis

  • 摘要:
      目的  为了解决船舶轴系振动信号的冗余信息过多、故障特征提取率较低等问题,提出一种基于经验小波变换与谱峭度(EWT-SK)的故障特征提取方法。
      方法  首先,利用经验小波变换(EWT)对原始信号进行处理,以分离冗余振动成分,从而解决经验模态分解(EMD)固有的端点效应和模态混叠问题;然后,基于谱峭度和相关系数,筛选模态函数并进行重构,以突出故障信息,从而提高信噪比;最后,利用谱峭度获得最优带通滤波器的参数,并对滤波之后的信号进行包络解调,从而完成故障诊断。
      结果  根据实例分析验证结果:在信号分解方面,EWT方法的特征提取稳定性和效率更高,可以保证轴系故障信息的完整性;在去噪效果方面,采用EWT-SK方法之后,故障信号的峭度值为4.761 6,相关系数为0.708 8,信噪比为3.762 4,其表现优于EMD和变分模态分解(VMD)方法。
      结论  EWT-SK方法具有良好的特征提取能力与噪声抑制能力,适用于船舶轴系的故障诊断。

     

    Abstract:
      Objectives  In order to solve the problems of redundant information and low fault feature extraction rate in the vibration signal of marine shafting, a fault feature extraction method based on empirical wavelet transform-spectral kurtosis (EWT-SK) is proposed.
      Methods  First, the original signal is processed by empirical wavelet transform (EWT) to eliminate the excessive vibration components. This method solves the inherent defects of empirical mode decomposition (EMD) such as the endpoint effect and mode aliasing. Second, the modal function is reconstructed based on kurtosis and correlation coefficient, highlighting fault information and improving the signal-to-noise ratio. Finally, the optimal bandpass filter parameters are obtained by spectral kurtosis and used to design filters, then the filtered signal envelope is demodulated to realize fault diagnosis.
      Results  According to the results of case analysis and verification, in the aspect of signal decomposition, EWT has higher stability and efficiency in feature extraction, enabling it to ensure the integrity of shafting fault information. In the aspect of the denoising effect, after using the EWT-SK method, the kurtosis value of the fault signal is 4.761 6, the number of the correlation coefficient is 0.708 8 and the signal-to-noise ratio is 3.762 4, which is better than EMD and variational mode decomposition (VMD).
      Conclusions  The EWT-SK method has good feature extraction ability and noise suppression ability, making it suitable for the fault diagnosis of marine shafting.

     

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