Quality variables are key indicators of the operating performance in industrial processes. Because they are difficult to measure, soft sensor models can be adopted to predict them timely. For accurate prediction, sufficient training data are necessary to construct a good soft sensor model. In practical industrial processes, however, data labeled with quality variables are usually deficient in the desired region. Particularly, when the process is just switched to a new mode, available data in this new mode are initially quite a few. In this paper, a novel data synthesis method based on the regressor-embedded semi-supervised variational autoencoder (RSSVAE) model is proposed to generate synthetic labeled data when the original labeled data are inadequate. The proposed model utilizes not only the original data in the data-scarce region but also the data in other regions, which share some common information with the scarce data. Meanwhile, data synthesis and model correction mechanism are implemented iteratively to avoid model biases. Once the synthetic labeled data of the data-scarce region are acquired, they are combined with the original labeled data to establish a local soft sensor and predict the quality variables of the unlabeled data. Finally, a real ammonia synthesis process is introduced to demonstrate the effectiveness of the proposed method.

质量变量是工业过程中运行性能的关键指标。由于它们难以测量，因此可以采用软传感器模型来及时预测它们。为了准确预测，需要足够的训练数据来构建良好的软传感器模型。然而，在实际的工业过程中，标有质量变量的数据在所需区域通常是有缺陷的。特别是当进程刚切换到一个新模式时，这个新模式中的可用数据最初是相当多的。在本文中，提出了一种基于回归器嵌入的半监督变分自编码器（RSSVAE）模型的新型数据合成方法，以在原始标记数据不足时生成合成标记数据。所提出的模型不仅利用了数据稀缺地区的原始数据，还利用了其他地区的数据，这些数据与稀缺数据共享一些共同的信息。同时，迭代实现数据合成和模型校正机制，以避免模型偏差。一旦获取到数据稀缺区域的合成标记数据，将它们与原始标记数据结合以建立局部软传感器并预测未标记数据的质量变量。最后，介绍了一个真实的氨合成过程，以证明所提出方法的有效性。一旦获取到数据稀缺区域的合成标记数据，将它们与原始标记数据结合以建立局部软传感器并预测未标记数据的质量变量。最后，介绍了一个真实的氨合成过程，以证明所提出方法的有效性。一旦获取到数据稀缺区域的合成标记数据，将它们与原始标记数据结合以建立局部软传感器并预测未标记数据的质量变量。最后，介绍了一个真实的氨合成过程，以证明所提出方法的有效性。