Fault Detection and Diagnosis of Continuous Process Based on Multiblock Principal Component Analysis

Abstract

The fault detection and diagnosis of continuous process is very important for the production safety and product quality. Owing to its no need to know much about the process mechanism and exact process model ,the data driven method, typically the principal component analysis (PCA) has attracted much attention by chemical researchers for monitoring process. PCA is powerful in fault detection ,however, it has difficulties in diagnosing fault correctly in complex process. In this paper, the multiblock principal component analysis (MBPCA) is applied for fault detection and diagnosis in continuous process, which uses the integral PCA to detect fault and block contribution and variables contribution to diagnose fault. The simulations on the Tennessee Eastman process show that the proposed method can not only detect fault quickly ,but also find the fault location exactly.