文献类型：期刊文献

英文题名：Nonlinear dynamic process monitoring based on DLLE-SVDD

作者：Ma, Yuxin[1]; Wang, Mengling[1]; Shi, Hongbo[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, 130 Meilong Road, Shanghai, China

年份：2012

起止页码：3131

外文期刊名：Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

收录：EI(收录号：20130415919549)

语种：英文

外文关键词：Feature extraction - Process monitoring - Process control - Data description - Learning algorithms - Matrix algebra

摘要：A novel process monitoring method for dynamic nonlinear industrial processes is proposed by combining dynamic Locally Linear Embedding(DLLE) with Support Vector Data Description(SVDD). Firstly, the data matrix is augmented taking correlation of the samples into consideration. Then, LLE manifold learning algorithm is performed for nonlinear dimensionality reduction and feature extraction. The mapping matrix from data space to feature space was calculated by using local linear regression which guarantees the real-time property. Next, in order to avoid the influence of noise and disturbance on the traditional statistics, the fault detection model is obtained based on SVDD in the feature space, in which a corresponding monitoring index and its control limit are determined. Finally, the feasibility and efficiency of the proposed method are shown through the TE process. ? 2012 IEEE.