Dynamic alarmprediction for critical alarms using a probabilisticmodel

Jianfeng Zhu; Chunli Wang; Chuankun Li; Xinjiang Gao; Jinsong Zhao

doi:10.1016/j.cjche.2016.04.017

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Dynamic alarmprediction for critical alarms using a probabilisticmodel

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

- Dynamic alarmprediction for critical alarms using a probabilisticmodel
- Dynamic alarmprediction for critical alarms using a probabilisticmodel
- 中国化学工程学报（英文版） 2016年24卷第7期页码：881-885
- Affiliations：
  
  1. Sinopec Research Institute of Safety Engineering,Qingdao,China,266071
  2. State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.04.017
  中图分类号：
- 纸质出版：2016
- Accepted：
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Jianfeng Zhu, Chunli Wang, Chuankun Li, 等. Dynamic alarmprediction for critical alarms using a probabilisticmodel[J]. 中国化学工程学报（英文版）, 2016,24(7):881-885.

Jianfeng Zhu, Chunli Wang, Chuankun Li, Xinjiang Gao, Jinsong Zhao. Dynamic alarmprediction for critical alarms using a probabilisticmodel[J]. , 2016, 24(7): 881-885.
Jianfeng Zhu, Chunli Wang, Chuankun Li, 等. Dynamic alarmprediction for critical alarms using a probabilisticmodel[J]. 中国化学工程学报（英文版）, 2016,24(7):881-885. DOI： 10.1016/j.cjche.2016.04.017.

Jianfeng Zhu, Chunli Wang, Chuankun Li, Xinjiang Gao, Jinsong Zhao. Dynamic alarmprediction for critical alarms using a probabilisticmodel[J]. , 2016, 24(7): 881-885. DOI： 10.1016/j.cjche.2016.04.017.

摘要

Abstract

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance

the operator will have more time to prevent them from happening. In this paper

we present a dynamic alarm prediction algorithm

which is a probabilistic model that utilizes alarm data from distributed control system

to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model

which occur because of the nonlinear relationships between variables. Finally

the dynamic alarm prediction algorithm is applied to an industrial case study.

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