The availability of manufacturing data is expected to grow exponentially due to the accelerating advancement in information technology, smart sensing, and industrial internet of things. To be able to efficiently leverage industrial “big data” to aid real-time decision making, smart manufacturing needs to incorporate field knowledge into the data-driven modeling process. When field knowledge of causality is available, causal graphical models are an effective way to incorporate it into a data-driven modeling process, leading to improved robustness and prediction power under distribution shifts. In this work, a process monitoring method based on causal graphical models and a multiple model framework is developed to detect clogging in a steel continuous casting process. By exploiting the statistical independence and invariance properties implied by the causal graphical model, this proposed method removes the effects of the confounding disturbance, leading to improved detection performance. Additionally, through a comparative experiment, it is shown that the causal graphical model is crucial to ensure that the disturbance is correctly removed while the information of interest is retained. This presented method is a good example of data-driven, knowledge-enabled models deployed in smart manufacturing.

由于信息技术、智能传感和工业物联网的加速发展，制造数据的可用性预计将呈指数级增长。为了能够有效地利用工业“大数据”来帮助实时决策，智能制造需要将现场知识融入数据驱动的建模过程中。当因果关系的领域知识可用时，因果图模型是将其整合到数据驱动的建模过程中的有效方法，从而提高分布变化下的鲁棒性和预测能力。在这项工作中，开发了一种基于因果图模型和多模型框架的过程监控方法来检测钢连铸过程中的堵塞。通过利用因果图模型所隐含的统计独立性和不变性特性，该方法消除了混杂干扰的影响，从而提高了检测性能。此外，通过比较实验表明，因果图模型对于确保在保留感兴趣的信息的同时正确去除干扰至关重要。这种提出的方​​法是智能制造中部署的数据驱动、知识支持模型的一个很好的例子。结果表明，因果图模型对于确保在保留感兴趣的信息的同时正确去除干扰至关重要。这种提出的方​​法是智能制造中部署的数据驱动、知识支持模型的一个很好的例子。结果表明，因果图模型对于确保在保留感兴趣的信息的同时正确去除干扰至关重要。这种提出的方​​法是智能制造中部署的数据驱动、知识支持模型的一个很好的例子。