Fault diagnosis of batch process based on denoising sparse auto encoder

Abstract

Sparse auto encoder(SAE) can reduces information loss and extract the meaningful feature by learning the deep structure of complex data. This paper presents a novel SAE based semi-supervised feature learning method for fault diagnosis of batch process which includes two stages, namely, unsupervised pre-training stage and supervised tuning stage. At the unsupervised pre-training stage, denoising SAE(DSAE) is utilized by introducing denoising auto encoder into SAE to improve the robustness of network. At the supervised tuning stage, the pretrained DSAE netwrok is optimized using back propagation algorithm to improve the accuracy of classification. The proposed method is validated on penicillin fermentation simulation experiment and Escherichia coli fermentation experiment. Experimental results show that the proposed approach achieves good fault diagnostic performance and is superirior to the traditional fault diagnosis method.