高级检索

    奇异值分解用于早期摩擦故障诊断的研究

    Research on the Application of Singular Value Decomposition in Early Friction Fault Diagnosis

    • 摘要: 为了提高早期摩擦故障诊断能力,引入奇异值分解方法。利用旋转机械振动信号周期性较强特点,由整周期采集到的信号构建Hankel矩阵,通过奇异值分解方法求取矩阵奇异值,并将奇异值分为大值区、中值区和小值区。结果表明:根据大值区奇异值可以重构出信号中主要的简谐周期分量,根据小值区奇异值可以重构信号中的噪声分量,根据中值区奇异值重构出的信号可以监测周期性冲击现象,而该特征为早期摩擦故障典型特征。结合摩擦故障实例进行分析,有效提取到了每周期2次的冲击特征,并进一步提取到了瞬时能量冲击现象,验证了该方法用于早期摩擦故障诊断的可行性。

       

      Abstract: To improve the early friction fault diagnosis capability, the singular value decomposition method was introduced. By utilizing the characteristic of strong periodicity of rotating machinery vibration signals, a Hankel matrix was constructed from the signals collected in a full cycle. The singular values of the matrix were obtained through the singular value decomposition method, and the singular values were divided into a large-value region, a medium-value region and a small-value region. Results show that the main harmonic periodic components in the signal can be reconstructed according to the singular values in the large-value region, the noise components in the signal can be reconstructed according to the singular values in the small-value region, and the signal reconstructed from the singular values in the medium-value region can monitor the periodic impact phenomenon, which is a typical feature of early friction faults. Combined with the analysis of a friction fault case, the impact feature of twice per cycle is effectively extracted, and the instantaneous energy impact phenomenon is further extracted, which verifies the feasibility of this method for early friction fault diagnosis.

       

    /

    返回文章
    返回