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Accident prevention by fault propagation analysis and causal fault diagnosis based on granger causality test

机译:基于Granger因果关系检验的故障传播分析和因果故障诊断预防事故

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Petrochemical plants have becoming increasingly large and automatic. There exist strong interdependencies between various units. Once any unit fails, it often triggers cascade failures as a chain reaction, resulting in significant production losses and catastrophic accidents (such as SEVESO, BOPHAL Disaster, etc.). Fault diagnosis methodologies can unveil early deviations in the fault causal chain. Two main issues exist that are how to describe the interdependency in such complex system, and how to discover the root causes of the current abnormal event to support maintenance. Meanwhile in order to reduce the fault impact to the plants, quickly diagnosis of the root cause of the fault is also quite necessary. In this paper, aiming to solve above issues, granger causality test is introduced to study the fault interdependency by analyzing the relationship between process parameters of petrochemical units and establishing an effect diagram of the process parameters. When alarm occurs on condition monitoring system, the effect relationship diagram of the process parameters is used to elect related process parameters which haven't exceed the alarming threshold but may indicate an incipient fault. Then the granger causality test is used on the selected parameters to do the test pairwise. According to the degree of the causal relationship of the process parameters, the fault quantitative cause and effect diagram can be established. By using the quantitative cause and effect diagram, the path with the biggest quantitative value of causal relationship can be considered as the most probable fault propagation path in the petrochemical units according to the current alarm. In this way the root cause of the alarm can be revealed easily. The pilot application for FCCU and atmospheric and vacuum distillation unit in the case studies validates the effectiveness of the proposed method and its application value in the petrochemical industry.
机译:石化厂已变得越来越大且自动化。各个单元之间存在很强的相互依赖性。一旦任何单元发生故障,它通常会作为连锁反应触发级联故障,从而导致重大的生产损失和灾难性事故(例如SEVESO,BOPHAL Disaster等)。故障诊断方法可以揭示故障因果链中的早期偏差。存在两个主要问题,即如何描述这种复杂系统中的相互依赖性,以及如何发现当前异常事件的根本原因以支持维护。同时,为了减少故障对工厂的影响,快速诊断故障的根本原因也是非常必要的。为了解决上述问题,本文引入格兰杰因果关系检验,通过分析石化装置工艺参数之间的关系并建立工艺参数效果图,研究故障的相互依赖性。当状态监测系统发生警报时,过程参数的效果关系图用于选择尚未超过警报阈值但可能指示出早期故障的相关过程参数。然后,对所选参数使用格兰杰因果关系测试以成对进行测试。根据过程参数之间的因果关系程度,可以建立故障定量因果图。通过使用定量因果图,根据当前警报,可以将因果关系的定量值最大的路径视为石化装置中最可能的故障传播路径。这样,可以很容易地发现警报的根本原因。在案例研究中,FCCU和常压和减压蒸馏装置的试点应用证明了该方法的有效性及其在石化行业中的应用价值。

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