Latent Vector Autoregressive Modeling with Maximum Predicted Variance for Dynamic Process Monitoring

In this paper, we define reduced dimensional predictors in latent dynamic systems in contrast to the traditional full-dimensional predictor models. Then the estimation of the new latent vector autoregressive model is developed with an objective to maximize the predicted variance for a given number of latent variables. A new dynamic predictive monitoring index that accounts for variations in the prediction residual and the predictor is developed. The residuals are modeled with a subsequent principal component analysis and a comprehensive monitoring method is developed to detect abnormal situations in industrial and operational systems. The new algorithm is tested on a simple closed-loop control system and the revamped Tennessee Eastman simulated process to show its effectiveness compared to other state-of-the-art methods.