Sufficient data are necessary for valid process monitoring results. However, modern industrial processes sometimes switch to new modes to meet the changes in market demand. The available data in such a new mode are initially quite scarce and it brings huge obstacles to data‐based model construction. In this paper, a novel data synthesis method based on variational autoencoders is proposed to generate synthetic data for the data‐scarce region. The proposed method utilizes not only the original data in the data‐scarce region but also the data in other data‐intensive regions, which share some common information with the scarce data. To avoid model biases caused by the data imbalance between these regions, a model correction mechanism is also developed. Once the ultimate synthetic data of the data‐scarce region are acquired, they are combined with the original data to establish a local monitoring model. Finally, the effectiveness of the proposed method is demonstrated through a real ammonia synthesis process.

中文翻译：

---

通过在数据密集型区域中传输信息来合成数据，以增强数据稀疏区域中的过程监视性能

要获得有效的过程监控结果，必须有足够的数据。但是，现代工业流程有时会切换到新模式以满足市场需求的变化。这种新模式下的可用数据最初非常匮乏，这给基于数据的模型构建带来了巨大的障碍。本文提出了一种基于变分自动编码器的新型数据合成方法，用于生成数据稀疏区域的合成数据。所提出的方法不仅利用数据稀缺区域中的原始数据，而且利用其他数据密集区域中的数据，这些数据与稀缺数据共享一些公共信息。为了避免由这些区域之间的数据不平衡引起的模型偏差，还开发了一种模型校正机制。一旦获得了数据稀缺区域的最终综合数据，将它们与原始数据结合起来以建立本地监视模型。最后，通过实际的氨合成工艺证明了所提出方法的有效性。