Process Monitoring Based on Recursive Probabilistic PCA for Multi-mode Process

Abstract

A recursive probabilistic principal component analysis (PPCA) based data-driven fault identification method is proposed to handle the missing data samples and the mode transition in multi-mode process. This model is recursively obtained by using the increasing number of normal observations with partly missing data. First, based on the singular value of historic data matrix, the whole process is divided into different steady modes and mode transitions. For steady modes, the conventional PPCA is used to obtain the principal components, and to impute the missing data. When the mode is a mode transition, the proposed recursive PPCA is applied, which can actually reveal the between-mode dynamics for process monitoring and fault detection. After that, in order to identify the faults, a contribution analysis method is developed and used to identify the variables which make the major contributions to the occurrence of faults. The effectiveness of the proposed approach is demonstrated by the Tennessee Eastman chemical process. The results show that the presented approach can accurately detect abnormal events, identify the faults, and it is also robust to mode transitions.