Fault diagnosis of microbial pharmaceutical fermentation process with non-Gaussian and nonlinear coexistence

摘要

A large Proportion of batch processes commonly have traits of non-Gaussian and nonlinear. In this work, Multiway Kernel Entropy Independent Component Analysis (MKEICA) algorithm was developed to formulate more accurate model for process monitoring so as to enhance the monitoring performance. The original process data with three-dimension were first expanded into two-dimensional data matrix by using AT variable expansion method. The Kernel Entropy Component Analysis (KECA) was then employed to preprocess the data in order to reduce data redundancy. Such approach can also retain the information of cluster structure and maximize the essential characteristics of data. After that, a monitoring model of MKEICA was established for production process monitoring. Once a fault is detected, a nonlinear contribution plots method would be utilized to diagnose the fault variables. Consequently, to illustrate the superiority and feasibility, the proposed method was conducted on the penicillin simulation platform and the actual pharmaceutical production process.