Reliable Soft Sensors With an Inherent Process Graph Constraint

Nowadays, data-driven models have been prevalent in predicting hard-to-measure key quality indicators of industrial processes in order to improve product quality and process safety. Such models are called soft sensors as they can serve the same role as physical sensors, but they do not require extra physical devices. Despite their success, soft sensors suffer from poor reliability. Reliability is the ability of soft sensors to give accurate predictions not only at the time they are trained, but also in the long term, despite potential drifts of the process. This is important when soft sensors are to be applied in critical industrial processes. In order to alleviate this problem, in this article, we propose a graph-constrained soft-sensor (GCSS) model that uses graph convolutions based on the a priori undirected graph of the process variables. Based on the modern control theory, we also propose an approach to extracting an undirected graph from process diagrams of the target process. This approach can identify relationships between process variables, which is easy to use and can be applied to a majority of industrial processes. The extracted graph structure serves as a constraint, and pushes the data-driven GCSS model into the direction of the true inner structure of the target process. With the aid of a priori graph knowledge, the GCSS model enjoys better generalizability and reliability. This has been validated in a simulation example and a real-world high-low transformer process. Compared to other soft sensors, the test performance of the GCSS model is improved by 6.5%. In the high-low transformer process, the GCSS model has the best test performance and the gap between training and test performance is reduced by 54%.