Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

软传感器广泛用于预测通常难以测量的质量变量。有必要构建一个自适应模型来应对过程的非平稳性。在这项研究中，基于具有潜在变量的贝叶斯网络，为非平稳过程设计了一种新的与质量相关的局部加权软感知方法。具体而言，提出了一种有监督的贝叶斯网络，其中提取了面向质量的潜在变量，并进一步应用于双层相似性测量算法。所提出的软感知方法试图通过与质量相关的信息为非平稳过程找到一种通用方法，其中详细解释了局部相似性和窗口置信度的概念。通过应用到数值示例和脱丁烷塔来证明所开发方法的性能。结果表明，所提出的方法在预测关键质量变量的准确性方面优于竞争方法。