基于混合编程和结合特征提取方法的转辙机故障诊断系统开发

针对基于EMD、WPT的特征提取方法各自存在的问题,采用将EMD与WPT结合的信号分解方法用于包络特征的提取,保证信号的分解不仅具有较高的分辨率,并且能够避免虚假IMF分量带来的错误信息。在包络特征的提取及结合之后,采用了基于PCA的特征选择方法对缺陷信号的特征数据集进行最有效特征的分类识别。根据以上特征提取及识别方法,使用Matlab以及Labview的混合编程进行了面向S700K-C型电动转辙机的故障诊断系统操作,并通过应用试验证明了该系统能够准确、快速地提取出故障信号。

Development of diagnosis system based on mixed programming and combined extraction method for switch machine

According to the problems existing in envelope feature extracting methods that were based on Wavelet Packet Transform (WPT) or Empirical Mode Decomposition (EMD), this paper adopts signal decomposition method of EMD and WPT combination in feature extracting. This decomposition method can not only provide high resolution, but also avoid error messages caused by false Intrinsic Mode Functions (IMF) components. After the combination of envelope features are extracted using EMD and WPT method, the most effective characteristics of defected signals will be selected and classified using the data feature selecting method based on Principal Components Analysis (PCA). Based on the above features extracting and selection methods, this paper uses mixed programming of Labview and Matlab to develop a diagnosis system for S700K-C electrical switch machine, and the diagnostic testing experiment in the end proves that this diagnosis system can extract defected signals accurately and rapidly.