本文介绍一种新的基于振动信号分析的状态识别方法,即Hilbert时频谱重心与支持向量机相结合的进行设备故障诊断的分类方法。根据信号的循环平稳性,采用同步平均对信号进行预处理,实现从时域到角度域的转换;之后进行经验模式分解,计算得到Hilbert时频谱;在此基础上计算Hilbert时频谱的重心,构建一个特征向量。最后采用支持向量机进行训练和学习,实现设备的状态识别。并以滚动轴承的状态识别为例证明此方法的有效性。研究表明,此方法有助于设备预知维修的发展。
In this paper, a new pattern recognition method for machine working condition based on gravity center of Hilbert time-frequency spectrum and support vector machine (SVM) is demonstrated in detail based on vibration signal analysis. Firstly, synchronous average is used on monitored signal for preprocessing, which is used to remove effect from cyclostationary characteristics. Vibration signal data information changes from time domain to angle domain, which is more suitable for rotary machine pattern recognition. Then, Hilbert time-frequency spectrum is obtained according to Empirical Mode Decomposition (EMD) and Hilbert transform. Thirdly, gravity center of Hilbert spectrum is calculated to construct a feature vector for machine working condition pattern recognition. In the end, different working condition can be classified by using support vector machine (SVM). Rolling bearing pattern recognition is as an example to verify the effectiveness of this method in the research. According to the result analysis, it can be concluded that this method will be helpful for the development of machine preventative maintenance.