Independent and Related Variable Fault Detection Based on Information Concentrated Variational Auto-encoder

Abstract

With the rapid development of deep learning methods, the variational auto-encoder (VAE) has been utilized for nonlinear process monitoring. However, most VAE-based methods hardly consider the inner independent and related relationship of each variable. To overcome this problem, a novel VAE named independent and related variable information concentrated variational auto-encoder (IRVIC-VAE) is proposed. To concentrate the independent and related information, a loading weight matrix regularization based on the mutual information of variables with gaussian distribution is introduced so that the variables can separate into two subspaces that contain independent and related information in latent variables. The original data space decomposed via IRVIC-VAE is orthogonal and approximate to normal distribution. For process monitoring, the independent variable space and related variable space are combined to establish two statistics according to Kullback-Leibler divergence and 2-norm. Finally, the performance and effectiveness of IRVIC-VAE are testified by Tennessee Eastman (TE) process.