文献类型：期刊文献

中文题名：基于自适应局部离群概率的动态过程监控(英文)

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

作者：马玉鑫[1];侍洪波[1];王梦灵[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2014

卷号：22

期号：7

起止页码：820

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;EI(收录号：20143518100348);Scopus;CSCD:【CSCD2013_2014】；

基金：Supported by the National Natural Science Foundation of China(61374140);Shanghai Postdoctoral Sustentation Fund(12R21412600);the Fundamental Research Funds for the Central Universities(WH1214039);Shanghai Pujiang Program(12PJ1402200)

语种：中文

中文关键词：异常概率;过程监控;自适应;连续搅拌釜式反应器;非高斯分布;概率算法;监测模型;工业过程

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.
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 movingwindow local outlier probability algorithm is proposed, lies 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.