Wigner-Ville分布在机械故障诊断中的研究

时频分布从时域特征与频域特征的结合途径揭示了信号的构成本质.文章介绍了基于WignerVille分布(WVD)的故障诊断方法,包括基于核函数抑制交叉项,时频分布与人工神经网络相结合,以及WVD的高阶谱.机械系统故障信号往往是非平稳的,联合时频分布是对故障信号分析的有力工具.WVD很高的能量聚集性和很好的时频分辨率,极大地提高了故障信号特征提取的准确度.

The Research of Wigner- Ville Distribution in Machine Fault Diagnosing

Quadratic time frequency uncover the feature of non-stationary signal on the basis of the combination of time domain and frequency domain. In this paper,a new fault diagnosing method based on WVD (Wigner-Ville Distribution ) was introduced,including cross term suppression based on kernel function, time-frequency distribution combining with Artificial Neural Network and Higher Order Spectrum of WVD. Machine fault signal was usually composed of non-stationary signals. Joint time frequency was a powerful tool to analyze fault signal. Due to high temporal and frequency resolution and high energy focus,the accuracy of fault signal feature extraction is greatly improved by WVD.