A comprehensive fault detection and diagnosis method for chemical processes

Abstract

Deep learning methods based on supervised learning have various applications in chemical process fault detection and diagnosis (FDD). However, these methods as black boxes are unable to provide guidance for process recovery, and the labels and fault samples available in actual chemical processes are insufficient for ideal supervised learning. In this study, eMixDTFCN containing FDD backbone network, semi-supervised learning framework, and explainable method is proposed for the first time to address the above challenges simultaneously. Dense temporal feature convolutional network (DTFCN) combining process variable embedding and dense temporal convolution is taken as the backbone network, where the sparse features from the high-dimensional embedded space are learned efficiently through the dense temporal convolution. MixMatch based on consistent regularization and entropy minimization enables DTFCN with semi-supervised learning and imbalanced learning. SHAP method trains an explainer to provide fault-related variables for process recovery. The high effectiveness and comprehensiveness of eMixDTFCN are validated in two typical chemical processes.