基于EEMD和SVM的冷轧机垂直振动相关故障的诊断

通过对冷板带轧机垂直振动过程的机理进行分析,结合轧机系统结构模型,建立含振动因素的冷轧机垂向系统动态轧制力模型.考虑复杂工况下,轧机在生产不同规格带钢时,由工艺参数波动等广义故障所致轧机垂直振动现象,基于工业现场数据进行数据驱动的故障诊断算法研究.采用集成经验模态分解算法对实测轧制力信号进行分解,选取有效的固有模态函数的能量作为特征向量,并将其输入到支持向量机分类器中,通过分类器对正常状态和故障状态进行区分,以实现轧机振动相关故障的准确诊断.

Vibration-Related Fault Diagnosis in Cold Rolling Mill by Using EEMD and SVM

Abstract： By analyzing the vibration process of cold rolling and using the structure model of the rolling system, a dynamic rolling force of the rolling vertical system was built, with the consideration of the influence of rolling vibration. A data-driven fault diagnosis was proposed based on industrial field data by using ensemble empirical mode decomposition (EEMD) and support vector machine (SVM), with the focus on the generalized fault, which were mostly caused by variations of process parameters under complex working conditions. According to the decoupling effect on measured rolling force data with the EEMD algorithm, the intrinsic mode function (IMF) component was defined as fault eigenvector and chosen as the input into the classifier of vector machine. Then, the vibration-related fault of cold rolling mills could be diagnosed by the distinction between the normal state and the fault state by SVM.