核主成分分析
过程(计算)
因果关系(物理学)
根本原因
断层(地质)
根本原因分析
故障检测与隔离
主成分分析
核(代数)
非线性系统
计算机科学
数据挖掘
人工智能
计量经济学
模式识别(心理学)
工程类
可靠性工程
数学
核方法
支持向量机
地质学
物理
组合数学
操作系统
地震学
执行机构
量子力学
作者
H. Gharahbagheri,Syed Imtiaz,Faisal Khan
摘要
Abstract Kernel principal component analysis (KPCA) based monitoring has good fault detection capability for nonlinear process systems; however, it can only isolate variables that have a contribution to the occurrence of a fault, and thus it is not precise in diagnosing. Since there is a cause and effect relationship between different variables in a process, accordingly a network‐based causality analysis method was developed for different fault scenarios to show causal relationships between different variables and to see the causal effect between the variables most contributing to the occurrence of a fault. It was shown that KPCA in combination with causality analysis is a powerful tool for diagnosing the root cause of a fault in the process. In this paper the proposed methodology was applied to a fluid catalytic cracking (FCC) unit and the Tennessee Eastman process to diagnose root causes for different faulty scenarios.
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