Bayesian network is a frequently-used uncertainty reasoning method, which systematically describes relations between random variables. Dynamic Bayesian network is an extension of Bayesian network, which contains the relations between variables at different times. Soft sensor is an important industrial application, in which feature variables are selected to predict the value of the target variables. For industrial soft sensor applications, dynamics is still a tough problem, particularly together with the uncertain feature of process data. In this article, DBN is employed for dynamic variable structure learning and transfer modeling to some strong regression models to build a soft sensor for dynamic industrial processes. At the beginning, a series of dynamic Bayesian networks are constructed on the training set with a sliding window. From these network structures we can find variables related to the quality variables. Then, in each time period, a sequence of data is compared with training data to select the most similar sequence by Dynamic Time Warping. Therefore, the structure of variables is built, i.e., the related feature variables of the quality variables can be determined by the network structure. In the regression step, the dynamic variable structure is transferred to some strong regressors, like Support Vector Regression and Adaboost for further regression. In case study, we use the debutanizer process and a low temperature transformer case to confirm the quality of the soft sensor method. The result reveals that, the prediction accuracy of the new method is much higher than the original commonly-used regression methods.

贝叶斯网络是一种经常使用的不确定性推理方法，它系统地描述随机变量之间的关系。动态贝叶斯网络是贝叶斯网络的扩展，它包含不同时间变量之间的关系。软传感器是重要的工业应用，其中选择特征变量以预测目标变量的值。对于工业软传感器应用，动力学仍然是一个棘手的问题，尤其是过程数据的不确定性。在本文中，DBN用于动态变量结构学习，并将建模转换为一些强大的回归模型，以构建用于动态工业过程的软传感器。最初，在带有滑动窗口的训练集上构建了一系列动态贝叶斯网络。从这些网络结构中，我们可以找到与质量变量相关的变量。然后，在每个时间段中，将数据序列与训练数据进行比较，以通过动态时间规整选择最相似的序列。因此，建立了变量的结构，即，可以通过网络结构来确定质量变量的相关特征变量。在回归步骤中，将动态变量结构传递给一些强大的回归器，例如支持向量回归和Adaboost，以进行进一步回归。在案例研究中，我们使用脱丁烷剂工艺和低温变压器外壳来确认软传感器方法的质量。结果表明，该新方法的预测精度比原来的常用回归方法要高得多。