【作者】 周福娜； 张玉； 杨书娜；

【Author】 ZHOU Funa;ZHANG Yu;YANG Shuna;Institute of Advanced Control and Intelligent Information Processing, Henan University;

【机构】 河南大学先进控制与智能信息处理研究所；

【摘要】 实际工业过程由于原料性质、外界环境等条件的变化,生产过程会出现多个稳定操作模态,稳定模态之间会经历一个缓慢变化的过渡模态。现有多模态处理方法没有对过渡模态的动态特性进行有效的过程监测,本文采用微分几何特征抽取的方法提取过渡模态的动态特征,主要是通过斜率、曲率等几何元素刻画过渡模态的动态曲线特征,建立起过渡模态基于滚动球的异常检测模型,进而进行过渡模态的异常监测。对稳态过程,则采用在线数据驱动的相关主元分析(correlative principal components analysis,CPCA)方法进行异常监测。本文方法与全局PCA、全局CPCA的仿真结果对比验证了该方法的有效性。更多还原

【Abstract】 Since the changes of raw material properties, external environment and other conditions, during practical industrial processes, multiple stable operation modes may arise, and between any two stable modes may undergo slowly changing transition modes. The existing multimode process monitoring methods haven’t monitored dynamic characteristics of the transition modes efficiently. This paper adopts differential geometry feature extraction method to extract the dynamic characteristics of transition modes, uses geometric elements, such as slope, curvature etc, to display the dynamic curve characteristics of transition modes, and then establishes the anomaly detection model of transition mode based on rolling balls to monitor the transition modes. The online data driven CPCA method is used for the anomaly detection of stable modes. Comparing this method with the global PCA and global CPCA, the experimental results show that the proposed method is efficient.更多还原