A Data-Driven Process Monitoring Approach Based on Evidence Reasoning Rule Considering Interval-Valued Reliability

Shanen Yu, Saijun Liu, Xu Weng (), Xiaobin Xu, Zhenjie Zhang, Fang Liu, Felix Steyskal and Georg Brunauer
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Shanen Yu: School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China
Saijun Liu: School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China
Xu Weng: School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China
Xiaobin Xu: School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China
Zhenjie Zhang: School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China
Fang Liu: School of Accounting, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Felix Steyskal: Maschinen-Umwelttechnik-Transportanlagen Gmbh, Schießstattgasse 49, 2000 Stockerau, Austria
Georg Brunauer: TU Wien, Institute for Energy Systems and Thermodynamics, Getreidemarkt 9, 1060 Vienna, Austria

Mathematics, 2022, vol. 11, issue 1, 1-18

Abstract: In the process industry, an alarm system is one of the important ways of condition monitoring. Due to the complexity and irregularity of process information in condition monitoring, there are too many false alarms in the current alarm system. In order to solve the problem of designing an alarm system, this paper proposes a multivariate alarm design method based on the evidence reasoning (ER) rule, considering interval-valued reliability, which can make full use of process information to make accurate alarm decisions. Firstly, the referential evidence matrixes (REMs) are constructed based on the training samples of process variables, and the real-time samples of the process variables are converted into alarm evidence by activating the REMs. Alarm evidence is then fused by the ER rule. In this fusion process, in order to better describe the uncertainty of the process information, the reliability of the alarm evidence is characterized by random variables with certain probability distributions, and it can be adjusted in dynamic intervals according to the real-time change of alarm evidence. Finally, the reactor fault case is implemented in the Tennessee Eastman (TE) process, which shows that the adjustment of interval-valued reliability can adapt to the irregular change of process information and obtains consistent alarm results to further improve the accuracy of alarm decisions.

Keywords: condition monitoring; ER rule; data-driven; evidence fusion; interval-valued reliability; decision-making (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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