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%.

中文翻译：

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具有固有过程图约束的可靠软传感器


如今，数据驱动模型已普遍用于预测工业过程中难以测量的关键质量指标，以提高产品质量和过程安全性。此类模型称为软传感器，因为它们可以起到与物理传感器相同的作用，但不需要额外的物理设备。尽管取得了成功，但软传感器的可靠性较差。可靠性是指软传感器不仅在训练时而且在长期内提供准确预测的能力，尽管过程中可能存在偏差。当软传感器应用于关键工业过程时，这一点非常重要。为了缓解这个问题，在本文中，我们提出了一种图约束软传感器（GCSS）模型，该模型使用基于过程变量的先验无向图的图卷积。基于现代控制理论，我们还提出了一种从目标过程的流程图中提取无向图的方法。该方法可以识别过程变量之间的关系，易于使用，并且可以应用于大多数工业过程。提取的图结构作为约束，将数据驱动的GCSS模型推向目标流程真实内部结构的方向。借助先验图知识，GCSS模型具有更好的通用性和可靠性。这已在仿真示例和现实世界的高低变压器过程中得到验证。与其他软传感器相比，GCSS模型的测试性能提高了6.5%。在高低变换过程中，GCSS模型具有最好的测试性能，训练和测试性能之间的差距缩小了54%。