FRFT在转子起停车状态评估中的应用研究进展

转子起停车过程中由传统的等时间间隔采样所获得的机械动态信号已不再保持原有的周期特性,采用经典的基于傅里叶变换的谱分析方法进行信号处理不再有效。针对转子起停车状态评估中幅值、频率的非平稳特性所带来的问题,引入分数阶傅里叶变换研究了基于起停车信息的故障定性分析和定量识别方法,提出了基于分数阶全息原理的转子起停车故障特征提取方法,并在此基础上实现了基于分数阶主分量原理的转子故障特征模式定量分类。实验验证表明,该方法解决了转子起停车过程中瞬变信息的获取问题,能有效提取出起停车过程振动信号中的典型故障特征,可以实现对转子不同故障类型及不同故障程度的准确分类。

Research Progress of Rotor Startup Estimation with FRFT

The periodic signals acquired from a rotor by equal-time-interval sampling stop being periodic during the startup or shutdown stages, making methods based on Fourier transform no longer applicable. To estimate vibration of a startup or shutdown rotor with both non-stationary amplitude and frequency, qualitative and quantitative analysis of a startup rotor was performed with fractional Fourier transform (FRFT), and a new rotor startup running estimation method was proposed. In this method, fault feature extraction of the rotor was performed with FRFT combined with the holographic principle, and feature classification was conducted with the fractional PCA method. Experimental results showed that the proposed method effectively solved the problem of extracting transient information in the startup process of a rotor, precisely extracting the fault feature, and accurately realizing fault feature quantitative classification of several different typical faults, as well as the same rotor fault at different degrees.