An Efficient Method to Discover Association Rules of Mode-Dependent Alarms Based on the FP-Growth Algorithm

被引：2

作者：

Wang, Kai Ru ^{[1
]}

Hu, Wenkai ^{[2
,3
]}

Chen, Tongwen ^{[1
]}

机构：

[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada

[2] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China

[3] China Univ Geosci, Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Peoples R China

来源：

2020 IEEE ELECTRIC POWER AND ENERGY CONFERENCE (EPEC) | 2020年

基金：

中国国家自然科学基金; 加拿大自然科学与工程研究理事会;

关键词：

Alarm monitoring; mode-dependent alarms; operating modes; data mining;

D O I：

10.1109/epec48502.2020.9320007

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

State-based alarming is an advanced technique to reduce nuisance alarms and alarm floods by suppressing alarms associated with certain conditions or operating modes. To implement this technique, the key is to find the associations between operating modes and alarms. In practice, this is mainly done based on the experience of plant operators and expert knowledge of process engineers, and thus is very time consuming. Therefore, this paper proposes a data driven method to discover the association rules of mode-dependent alarms for both single and multiple operating modes from the historical Alarm and Event (A&E) logs in an efficient way based on a data mining approach named FP-Growth. The effectiveness of the proposed method is demonstrated by an industrial case study.

引用

页数：5