Multi-rate Gaussian Bayesian network soft sensor development with noisy input and missing data

Abstract

For efficient process control and monitoring, accurate real-time information of quality variables is essential. To predict these quality (or slow-rate) variables at a fast-rate, in the industry, inferential/soft sensors are often used. However, most of the conventional methods for soft sensors do not utilize prior process knowledge even if it is available. The prediction accuracy of these inferential sensors depends mainly on the quality of available data, which can be affected by significant noise and possible sensor failures. To address these issues, in this work, a generic Gaussian Bayesian network based soft-sensor framework is developed, which can account multiple hidden states and multirate/missing data. In the proposed framework, due to the presence of hidden variables and missing data, posterior probability of these variables in E-step of the EM algorithm is evaluated using Bayesian inference. Compared to the existing soft-sensors, the proposed approach will allow users to integrate prior knowledge into the BN structure. Moreover, due to the probabilistic nature of BNs, variances of measurement noises and disturbances between hidden states are simultaneously estimated. The proposed framework is generic and can be used for any multi-layered structure. Its performance is demonstrated for two different structures, two-layer and multilayered structures, on a benchmark flow-network problem and an industrial process. It is observed that the proposed Gaussian Bayesian network based soft sensors are able to give significantly better and more reliable estimates compared to the conventional approaches.