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Accident Prevention by Fault Propagation Analysis and Causal Fault Diagnosis Based on Granger Causality Test

机译:基于格兰杰因果关系测试的故障传播分析和因果故障诊断事故预防

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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:石化植物变得越来越大,自动。各个单位之间存在强大的相互依赖性。一旦任何单位发生故障,它通常会触发级联故障作为链式反应,导致显着的生产损失和灾难性事故(如塞福,骨折灾害等)。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。同时为了减少对植物的故障影响,快速诊断故障的根本原因也是必要的。本文旨在解决上述问题,通过分析石化单位工艺参数与建立工艺参数的效果图来研究故障相互依赖性研究故障相互依赖性。当在条件监控系统上发生警报时,过程参数的效果关系图用于选用相关的过程参数,这些参数缺口阈值并表示初始故障。然后,GRANGER因果关系测试用于所选参数以进行测试成对。根据过程参数的因果关系的程度,可以建立故障定量原因和效果图。通过使用定量原因和效果图,具有最大的因果关系的定量值的路径可以被视为根据当前警报的石化单元中最有可能的故障传播路径。以这种方式,可以容易地揭示警报的根本原因。在案例研究中,FCCU和大气和真空蒸馏装置的试验申请验证了拟议方法的有效性及其在石油化工行业中的应用价值。

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