文献类型：期刊文献

中文题名：Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

英文题名：Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

作者：Fan Wang[1];Honglin Zhu[1];Shuai Tan[1];Hongbo Shi[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

年份：2016

卷号：24

期号：7

起止页码：856

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;;Scopus;CSCD:【CSCD2015_2016】；

基金：Supported by the National Natural Science Foundation of China(61374140,61403072)

语种：英文

中文关键词：Multimode processFault detectionHidden Markov modelOrthogonal nonnegative matrix factorization

外文关键词：马尔可夫模型;非负矩阵分解;监测模型;隐藏;多模;正交;故障检测方法;对数似然概率

摘要：Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy.The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.
Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively, this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization （ONMF） and hidden Markov model （HMM）. The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy. The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indi- cation named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.