This paper presents a novel methodology for dynamic risk analysis, integrating the multivariate data‐based process monitoring and logical dynamic failure prediction model. This concept for dynamic risk analysis is comprised of the fault assessment and dynamic failure prognosis modules. A combination of the naïve Bayes classifier, Bayesian network, and event tree analysis is utilized to manifest the concept. The naïve Bayes classifier is used for fault detection and diagnosis; it also generates a multivariate probability for a fault class in each time‐step, which is used for dynamic failure prognosis by different paths a fault can lead a process to failure. The proposed framework has been applied to two process systems: a binary distillation column and the RT 580 experimental setup in four fault scenarios, and it is found the developed technique can effectively monitor the process and predict the failure.

中文翻译：

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故障检测和动态风险评估的新型数据驱动方法

本文提出了一种新的动态风险分析方法，将基于数据的多元过程监控和逻辑动态故障预测模型相集成。动态风险分析的概念由故障评估和动态故障预测模块组成。朴素的贝叶斯分类器，贝叶斯网络和事件树分析相结合来体现这一概念。朴素的贝叶斯分类器用于故障检测和诊断；它还在每个时间步中生成故障类别的多元概率，该概率用于通过故障可能导致过程失败的不同路径进行动态故障预测。拟议的框架已应用于两个过程系统：双蒸馏塔和RT 580实验装置，在四种故障情况下，