FAULT DETECTION AND DIAGNOSIS BY SUPPORT VECTOR MACHINES: APPLICATION TO VINYL-CHLORIDE-MONOMER PROCESS

IF 0.2 Q4 MULTIDISCIPLINARY SCIENCES Suranaree Journal of Science and Technology Pub Date : 2023-12-13 DOI:10.55766/sujst-2023-03-e03028
C. Panjapornpon, Siriwatida Srirabai
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引用次数: 0

Abstract

Monitoring process status and identifying process operational faults are essential for improving the process safety in petrochemical plants that interactions between various process streams and units are associated. This paper presents a deployment of a support vector machine technique for detecting and identifying operational fault cases with a case study of a vinyl chloride monomer plant. An integrated simulation environment between MATLAB and UniSim Design dynamic simulator is utilized for evaluating the performance of the proposed fault detection and identification framework. Under the real-time software-in-the-loop simulation, the confusion matrix results and receiver operating characteristics supported that the proposed framework provides high accuracy of fault classification.
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支持向量机的故障检测和诊断:在氯乙烯单体工艺中的应用
监控工艺状态和识别工艺运行故障对于提高石化工厂的工艺安全至关重要,因为各种工艺流和装置之间存在着关联。本文以氯乙烯单体工厂为例,介绍了支持向量机技术在检测和识别操作故障案例中的应用。本文利用 MATLAB 和 UniSim Design 动态模拟器之间的集成模拟环境来评估所提出的故障检测和识别框架的性能。在实时软件在环仿真下,混淆矩阵结果和接收器工作特性证明了所提出的框架具有较高的故障分类准确性。
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来源期刊
Suranaree Journal of Science and Technology
Suranaree Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
0.30
自引率
50.00%
发文量
0
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