In this brief, a hierarchical Bayesian network modeling framework is formulated for large-scale process monitoring and decision making, which includes a basic layer and a functional layer. First, the whole process is decomposed into different units, where local Bayesian networks are constructed, providing monitoring information and decision-making capability for the upper layer. The network structure is determined automatically based on the process data in each local unit of the basic layer. Then, through incorporating the topological structure of the process, a functional Bayesian network is further constructed to infer the information from the basic layer, which can be customized according to user demands, such as fault detection, fault diagnosis, and classification of operating status. The performance of the proposed method is evaluated through a benchmark process.

中文翻译：

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用于大规模过程监控和决策的分层贝叶斯网络建模框架

在此简介中，为大型过程监视和决策制定了分层的贝叶斯网络建模框架，该框架包括一个基础层和一个功能层。首先，将整个过程分解为不同的单元，在其中构建本地贝叶斯网络，从而为上层提供监视信息和决策能力。网络结构是根据基础层每个本地单元中的过程数据自动确定的。然后，通过合并过程的拓扑结构，进一步构造功能性贝叶斯网络以从基础层推断信息，可以根据用户需求（例如，故障检测，故障诊断和操作状态分类）进行自定义。