基于EMD-AR和关联维数的轧机主传动系统故障诊断研究

Study on fault diagnosis of rolling mill main drive system based on EMD-AR and correlation dimension

为提高轧机主传动系统故障诊断的精度,提出了一种基于EMD-AR模型和关联维数的故障特征提取算法。该方法采用小波滤波和EMD对振动信号进行去噪和平稳化预处理,再对包含主要故障信息的本征模函数(IMF)分别建立AR模型进行降维,最后通过时延相图法重构AR模型参数的相空间,并计算其关联维数。实验分析表明:该算法不仅能够深刻、全面地表达动态系统状态变化的客观规律,而且实现了系统状态特征的分离,从而为有效地判断轧机主传动系统的故障状态和故障类型提供可靠的依据。

In order to improve the fault diagnosis precision of rolling mill main drive system,a fault feature extraction algorithm based on EMD-AR model and correlation dimension is proposed.In the proposed wave-let and,EMD are used to decompose the vibration signal of complex machine into several intrinsic mode function（IMF）,the AR models of some IMF components which contain main fault information are constructed.The correlation dimensions of auto-regressive parameters in AR models are calculated.Analysis results of the experimental data show that this method not only can reflect the state changes of dynamic system profoundly and comprehensively,but also can realize separation of the state features.It provides reliable basis for judging the fault conditions of rolling mill main drive system effectively.