Autoregressive Dynamic Latent Variable Models for Process Monitoring

In most industrial processes, both autocorrelations and cross correlations in the data need to be extracted for the purpose of process monitoring and diagnosis. However, traditional dynamic modeling methods focus on the dynamic relationship while the cross correlations are at best implicit. In this brief, a new autoregressive dynamic latent variable model is proposed to capture both dynamic and static relationships simultaneously. The proposed method is a rather general dynamic model which can improve the performance of modeling and process monitoring. The Kalman filter and smoother are employed for inference while model parameters are estimated with an expectation-maximization algorithm. The corresponding fault detection method is also developed and a numerical example and the Tennessee Eastman benchmark process are used to evaluate the performance of the proposed model.