Decision theory on key-performance-indicator-based process monitoring and fault diagnosis approaches

被引：0

作者：

Zhou, Hao ^{[1
]}

Ma, Hengbo ^{[1
]}

Shen, Xinrui ^{[2
]}

机构：

[1] Harbin Inst Technol, Harbin 150001, Peoples R China

[2] Bohai Univ, Jinzhou 121013, Peoples R China

来源：

2017 SEVENTH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST2017) | 2017年

关键词：

Key performance indicators; Process monitoring; Decision theory; Multivariate statistics; Fault detection; MULTICRITERIA; PREDICTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, decision theory is introduced to key performance indicator (KPI) related process monitoring and fault diagnosis (PM-FD) field in order to settle the difficulties of making the most rational decision among several candidates. Considering that there are several evaluation criteria for PM-FD approaches, this paper provides some methods to help decision makers to find the most effective PM-FD approach among lots of alternatives when a few criteria are taken into consideration. Also, these methods are applied in the simulation study, including a numerical example and Tennessee Eastman process (TEP) benchmark.

引用

页码：438 / 443

页数：6

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