LAUSR

Abstract 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.