A MIIPCR Fault Detection Strategy for TEP

Abstract

Multivariate statistical method is one of data-driven fault diagnosis methods, which is widely used in complex industrial systems to realize faults detection. And, the common methods include the principal component analysis, principal component regression, and partial least squares (PLS). Compared with the PLS, improved principal component regression (IPCR) improves the alarm performance in the quality-related and quality-independent parts. Considering the advantage of the mutual information is good for selecting quality-related variables for modeling, the mutual information is employed to improve the principal component regression (MIIPCR). Through the design and analysis of the algorithms, the MIIPCR can give a performance boost for the principal components, especially for the IPCR in fault detection of Tennessee Eastman process (TEP). Compared with the IPCR, the MIIPCR has the strong advantage to improve feedback failures five and seven of the TEP. In addition, the MIIPCR also could apply to detect the other faults and confirm the higher than the IPCR. And for the unrelated faults, the MIIPCR also has the great identification ability. Lots of simulation had been on for the TEP, the simulation results showed that it's the great effectiveness of the MIIPCR.