Batch Process Monitoring and Fault Diagnosis Based on Multi-Time-Scale Dynamic PCA Models

Abstract

Dynamics are inherent characteristics of batch processes, which can be divided into short time-scale dynamics within a batch duration and long time-scale dynamics across several batches. The interactions between process variables make different types of dynamics confounded. Under such situations, it is difficult to perform efficient fault diagnosis. In this paper, a batch process monitoring scheme is proposed to separate different types of process variations for modeling and perform monitoring and fault diagnosis with multi-time-scale dynamic principal component analysis (PCA) models. Simulation results show that the fault diagnosis efficiency is enhanced.