基于稀疏特征的连轧机故障信号分离方法

Blind Source Separation Based on Sparse Feature for the Fault Signals of the Continuous Mills

连轧机组的稳定性对于保障轧制产品的质量精度起着决定性的作用,连轧机组中监测各轧机状态的信号具有强耦合性,从复杂的信号中分离出各轧机独立的状态信号,对连轧机组的状态监测和故障诊断具有重要的意义。提出了一种基于稀疏特征的连轧机故障信号分离方法,并进行了仿真和现场验证。首先,通过基于时频谱分割的稀疏分解方法将各混合信号中的微弱冲击特征提取出来;其次,对所有稀疏表示信号的原子按照一定规律排序,得到各混合信号的稀疏矩阵;然后,根据稀疏原子的相似性对稀疏表示的原子进行聚类,确定盲源分离的源个数;最后,根据稀疏矩阵的系数和源个数比较准确地估计出混叠矩阵,实现混合信号的盲分离。

The stability of a rolling mill plays a decisive role in the quality of rolled products,but signals monitoring the status of the rolling mill have very strong coupling.In this case,to separate the complex signals into independent status signals of each mill is essential to realize the status monitoring and fault diagnosis for rolling mill.In this paper,a technical scheme for separating fault signals of continuous rolling mills based on sparse features is proposed,and simulation and field verification are carried out.First,the weak impulses of the mixed signals are extracted through the method of sparse decomposition based on spectrum segmentation;second,the selected atoms are sorted out according to certain rules to get the sparse matrix of mixed signals;then,the selected atoms are clustered according to the similarity of sparse atoms to determine the number of blind sources;finally,the mixing matrix is estimated according to the sparse matrix and the number of blind sources to realize the blind source separation of mixed signals.