Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process

Abstract

Current fault detection methods based on deep neural networks only consider process information and ignore quality indicators. In order to obtain features representing both process variables and quality indicators efficiently, this paper designs teacher and supervise dual stacked auto-encoder (TSSAE) for quality-relevant fault detection in industrial process which separates the feature extraction and model construction. To separate the feature extraction and model construction, a mixing stacked auto-encoder which consists of a nonlinear encoder and a linear decoder is designed to extract features of process variables and quality indicators. Another encoder is supervised by the extracted features and further predict the process variables and quality indicators only from process variables. Then quality-relevant, quality-irrelevant and residual subspaces are constructed in a linear way and fault detection is implemented in these subspaces based on Euclidean distance and kernel density estimation. Finally, the effectiveness of TSSAE is evaluated by a numerical example and the Tennessee-Eastman process.