文献类型：会议论文

英文题名：Adaptive Local Outlier Probability for Dynamic Process Monitoring

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

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

会议论文集：24th Chinese Process Control Conference (CPCC)

会议日期：AUG 03-04, 2013

会议地点：Hohhot, PEOPLES R CHINA

语种：英文

外文关键词：Time-varying; Complex data distribution; Local outlier probability; Multi-mode; Fault detection

摘要：Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient moving window local outlier probability algorithm is proposed. Its key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor. (C) 2014 Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.