LAUSR

Multivariate statistical process monitoring (MSPM) has been widely used in modern industries and most of traditional MSPM methods are developed using uniformly sampled measurements. However, process variables are often sampled with different rates in practical industries. On the other hand, most of the industries are dynamic processes in which the measurements are highly autocorrelated. Thus, it is difficult to build a dynamic process model with incomplete data sets in multirate processes. In this paper, a multirate linear Gaussian state-space model is exploited to deal with the above issues. Both the offline model training and online process monitoring schemes are developed in the present of incomplete multirate process data sets. The proposed method is validated through a numerical example and the Tennessee Eastman benchmark process. Note to Practitioners—Process monitoring is essential to industrial process maintenance and safety. Typical monitoring methods focus on uniformly sampled variables which are rarely satisfied in practical industries. On the other hand, most industries are dynamic processes which bring more difficulties to process monitoring with multirate variables. This paper proposed a new scheme for multirate dynamic process monitoring based on the multirate linear Gaussian state-space model. To fully implement this approach: 1) the multirate process data need to be well divided into multiblocks. Each of the block should contain at least one variable; 2) the amount of latent variable’s dimension needs to be properly determined; and 3) the sampling rates of the process cannot be extremely distinguished otherwise will lead to a poor model performance. Two cases studied on both numerical example and Tennessee Eastman benchmark are given to validate the efficiency of the proposed algorithm on multirate dynamic process monitoring.