A new process monitoring method based on noisy time structure independent component analysis

Lianfang Cai; Xuemin Tian

doi:10.1016/j.cjche.2014.10.006

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A new process monitoring method based on noisy time structure independent component analysis

过程系统工程与过程安全 | Updated：2026-01-06

- A new process monitoring method based on noisy time structure independent component analysis
- A new process monitoring method based on noisy time structure independent component analysis
- 中国化学工程学报（英文版） 2015年23卷第1期页码：162-172
- Affiliations：
  
  College of Information and Control Engineering, China University of Petroleum,Qingdao,China,266580
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.10.006
  中图分类号：
- 纸质出版：2015
- Accepted：
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Lianfang Cai, Xuemin Tian. A new process monitoring method based on noisy time structure independent component analysis[J]. 中国化学工程学报（英文版）, 2015,23(1):162-172.

Lianfang Cai, Xuemin Tian . A new process monitoring method based on noisy time structure independent component analysis[J]. Chin.J.Chem.Eng., 2015, 23(1): 162-172.
Lianfang Cai, Xuemin Tian. A new process monitoring method based on noisy time structure independent component analysis[J]. 中国化学工程学报（英文版）, 2015,23(1):162-172. DOI： 10.1016/j.cjche.2014.10.006.

Lianfang Cai, Xuemin Tian . A new process monitoring method based on noisy time structure independent component analysis[J]. Chin.J.Chem.Eng., 2015, 23(1): 162-172. DOI： 10.1016/j.cjche.2014.10.006.

摘要

Abstract

Conventional process monitoring method based on fast independent component analysis (FastICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of themeasurement noises. In this paper

a newprocessmonitoring approach based on noisy time structure ICA (NoisyTSICA) is proposed to solve such problem. A NoisyTSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components (ICs). Subsequently

a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly

a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed NoisyTSICA-based monitoring method outperforms the conventional FastICA-based monitoring method.

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