文献类型：期刊文献

英文题名：Process monitoring with global probability boundary-based on Gaussian mixture model

作者：Wu, Qun[2]; Du, Wenli[2]; Qian, Feng[2]; Ma, Qingsong[1]

机构：[1] Electric and Instrument Dept., China Huanqiu Contracting and Engineering Corp., 807 Zhaojia-bang Rd, Shanghai, China; [2] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, 130 Meilong Rd, Shanghai, China

年份：2013

起止页码：789

外文期刊名：IEEE International Conference on Control and Automation, ICCA

收录：EI(收录号：20133516657875)

语种：英文

外文关键词：Gaussian distribution - Maximum principle - Image segmentation - Process control

摘要：Considering that the operation data hardly follow a uniform Gaussian distribution in complex industrial process, the Gaussian mixture model (GMM) is utilized as the tool of process monitoring in this article. The classic expectation maximization (EM) algorithm is adopted to estimate the model parameters, which often results in the model structural re-dundancy. Thus the consolidation operator is proposed and introduced to Figueiredo-Jain algorithm that is an advanced method of EM. The new approach can automatically optimize the number of Gaussian components on one hand, and solve the poor convergence problem of F-J method when Gaussian components overlapping too much during initialization on the other. With the obtained model, a criterion based on global probability is exploited for the real-time process monitoring. The validity and effectiveness of the proposed approach are illustrated through the coal-water slurry gasification control system. ? 2013 IEEE.