Industrial process data visualization based on a deep enhanced t-distributed stochastic neighbor embedding neural network

Abstract

Purpose

The purpose of this paper is to propose a approach for data visualization and industrial process monitoring.

Design/methodology/approach

A deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph.

Findings

The proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring.

Originality/value

This paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

Citation

Lu, W. and Yan, X. (2022), "Industrial process data visualization based on a deep enhanced t-distributed stochastic neighbor embedding neural network", Assembly Automation, Vol. 42 No. 2, pp. 268-277. https://doi.org/10.1108/AA-09-2021-0123