PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

Wende Tian; Guixin Zhang; Xiang Zhang; Yuxi Dong

doi:10.1016/j.cjche.2017.10.027

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PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

Selected Papers from the Chinese Process Systems Engineering Annual Meeting 2017 | Updated：2026-01-06

- PCA weight and Johnson transformation based alarm threshold optimization in chemical processes
- PCA weight and Johnson transformation based alarm threshold optimization in chemical processes
- 中国化学工程学报（英文版） 2018年26卷第8期页码：1653-1661
- Affiliations：
  
  College of Chemical Engineering, Qingdao University of Science & Technology,Qingdao,China,266042
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.10.027
  中图分类号：
- 纸质出版：2018
- Accepted：
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Wende Tian, Guixin Zhang, Xiang Zhang, 等. PCA weight and Johnson transformation based alarm threshold optimization in chemical processes[J]. 中国化学工程学报（英文版）, 2018,26(8):1653-1661.

Wende Tian, Guixin Zhang, Xiang Zhang, Yuxi Dong. PCA weight and Johnson transformation based alarm threshold optimization in chemical processes[J]. Chin.J.Chem.Eng., 2018, 26(8): 1653-1661.
Wende Tian, Guixin Zhang, Xiang Zhang, 等. PCA weight and Johnson transformation based alarm threshold optimization in chemical processes[J]. 中国化学工程学报（英文版）, 2018,26(8):1653-1661. DOI： 10.1016/j.cjche.2017.10.027.

Wende Tian, Guixin Zhang, Xiang Zhang, Yuxi Dong. PCA weight and Johnson transformation based alarm threshold optimization in chemical processes[J]. Chin.J.Chem.Eng., 2018, 26(8): 1653-1661. DOI： 10.1016/j.cjche.2017.10.027.

摘要

Abstract

To alleviate the heavy load of massive alarm on operators

alarm threshold in chemical processes was optimized with principal component analysis (PCA) weight and Johnson transformation in this paper. First

few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially

the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second

raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level (normally one alarm per minute). Finally

variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

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