Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

doi:10.1016/j.cjche.2016.01.016

›› 2016, Vol. 24 ›› Issue (7): 856-860.DOI: 10.1016/j.cjche.2016.01.016

• Process Systems Engineering and Process Safety • 上一篇下一篇

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

Fan Wang, Honglin Zhu, Shuai Tan, Hongbo Shi

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

收稿日期:2015-06-09 修回日期:2015-12-02 出版日期:2016-07-28 发布日期:2016-08-17
通讯作者: Hongbo Shi
基金资助:
Supported by the National Natural Science Foundation of China (61374140, 61403072).

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

Fan Wang, Honglin Zhu, Shuai Tan, Hongbo Shi

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

Received:2015-06-09 Revised:2015-12-02 Online:2016-07-28 Published:2016-08-17
Supported by:
Supported by the National Natural Science Foundation of China (61374140, 61403072).

摘要/Abstract

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 fromdifferent processmodes. ThemultipleHMMs for various operating modes lead to highermodeling accuracy. The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can bewell 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.

关键词: Multimode process, Fault detection, Hidden Markov model, Orthogonal nonnegative matrix factorization

Abstract: 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 fromdifferent processmodes. ThemultipleHMMs for various operating modes lead to highermodeling accuracy. The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can bewell 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.

Key words: Multimode process, Fault detection, Hidden Markov model, Orthogonal nonnegative matrix factorization

Fan Wang, Honglin Zhu, Shuai Tan, Hongbo Shi. Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring[J]. , 2016, 24(7): 856-860.

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Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

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