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

Fan Wang; Honglin Zhu; Shuai Tan; Hongbo Shi

doi:10.1016/j.cjche.2016.01.016

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2016年24卷第7期页码：856-860
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.01.016
  中图分类号：
- 纸质出版：2016
- Accepted：
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Fan Wang, Honglin Zhu, Shuai Tan, 等. Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring[J]. 中国化学工程学报（英文版）, 2016,24(7):856-860.

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.
Fan Wang, Honglin Zhu, Shuai Tan, 等. Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring[J]. 中国化学工程学报（英文版）, 2016,24(7):856-860. DOI： 10.1016/j.cjche.2016.01.016.

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. DOI： 10.1016/j.cjche.2016.01.016.

摘要

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.

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