t-SNVAE: Deep Probabilistic Learning With Local and Global Structures for Industrial Process Monitoring

Abstract

Variational autoencoder (VAE) is a generative deep learning (DL) model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t-SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN embedding. Through minimizing the Kullback–Leibler divergence of joint probabilities between the original data and the reconstructed data, VAE learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process.