Fault detection and diagnosis method for batch process based on ELM-based fault feature phase identification

Abstract

Because of the multiplicity of operation phases in batch process, which have specific control objects, different dominant process variables and distinct process correlation characteristics, the faults may also have phase characteristic. To conduct fault diagnosis for batch process more precisely, this paper proposes a fault detection and diagnosis method based on fault feature phase identification results. Firstly, extreme learning machine is used to identify fault feature phases between the faulty data set and the normal data set. Then, focusing on the different data nature implied in different fault feature phases, several ‘short stages’ are partitioned for the whole batch. After that, different multiway fisher discriminant analysis (MFDA) models are developed for these ‘short stages,’ respectively. The proposed method can deepen the search space analyzed by fault diagnosis into specific fault feature phases, which not only overcome the disadvantage of too many models in MFDA, but also overcome the disadvantage of low diagnosis accuracy and high false recognition rate of traditional MFDA method. Simulation results show the feasibility and validity of the proposed method.