摘要
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Wigner -Ville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。
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.
出处
《制造技术与机床》
CSCD
北大核心
2004年第7期24-28,共5页
Manufacturing Technology & Machine Tool
