Data-driven process monitoring has gained increasing attention because of the increasing demand in process safety and the rapid advancement of data gathering techniques. When monitoring a plant-wide multiunit process, establishing a monitor for each unit individually ignores the correlations among units, whereas establishing a global monitor for the entire process ignores the local process behavior. A variational Bayesian-based probabilistic modeling approach is proposed for efficient distributed process monitoring. A novel probabilistic latent variable model is developed to characterize the variable relationship in each local unit and among units. First, variational Bayesian-based latent variable extraction is performed in each local unit, through which variable relationship within a local unit is characterized. Second, variational Bayesian-based regression model is established between the latent variables and neighboring variables, through which the variable relationship among units is characterized. Then, modeling residuals and monitoring statistics are generated, through which the process status and the type of a detected fault are identified. The effectiveness of the proposed probabilistic modeling and monitoring method is verified by three case studies, including a numerical example, the Tennessee Eastman benchmark process, and a laboratory distillation process.

由于对过程安全性的需求不断增加以及数据收集技术的迅速发展，数据驱动的过程监控已引起越来越多的关注。在监视整个工厂的多单元过程时，为每个单元建立一个监视器会单独忽略单元之间的相关性，而为整个过程建立一个全局监视器则会忽略本地过程行为。提出了一种基于贝叶斯变分的概率建模方法，以进行有效的分布式过程监控。开发了一种新颖的概率潜在变量模型来表征每个局部单元和单元之间的变量关系。首先，在每个局部单元中执行基于变数贝叶斯的潜在变量提取，从而表征局部单元内的变量关系。第二，在潜在变量和相邻变量之间建立基于变分贝叶斯的回归模型，通过该模型来表征单位之间的变量关系。然后，生成建模残差和监视统计信息，从而识别过程状态和检测到的故障的类型。通过三个案例研究验证了所提出的概率建模和监视方法的有效性，其中包括一个数值示例，田纳西·伊士曼基准过程和实验室蒸馏过程。通过它可以识别过程状态和检测到的故障的类型。通过三个案例研究验证了所提出的概率建模和监视方法的有效性，其中包括一个数值示例，田纳西·伊士曼基准过程和实验室蒸馏过程。通过它可以识别过程状态和检测到的故障的类型。通过三个案例研究验证了所提出的概率建模和监视方法的有效性，其中包括一个数值示例，田纳西·伊士曼基准过程和实验室蒸馏过程。