Deep Discriminative Representation Learning for Nonlinear Process Fault Detection

Abstract

Nonlinear process fault detection remains a challenge, with representation learning being a key step. In this article, a deep neural network (DNN)-based discriminative representation learning approach is proposed to achieve efficient fault detection for nonlinear plant-wide processes. An early-stage fault rarely affects several independent variables concurrently; hence, mutual information-based block division and randomized fault construction are performed to generate faulty validation data. By using the training data from the normal operation training data and the constructed validation data, a DNN with stacked autoencoders and a softmax classifier is trained to generate discriminative representations that maximize the capability of discriminating normal and abnormal statuses. Finally, on the basis of the learned deep discriminative representations, support vector data description is employed to discriminate the normal and abnormal process statuses. The proposed monitoring approach is tested on a numerical example and an industrial tail-gas treatment process, through which the efficiency is verified. Note to Practitioners —A modern process is generally characterized by a large scale and complex nonlinear correlation, and monitoring of such nonlinear plant-wide processes is imperative. Nowadays, a large amount of process data is generally available, and deep neural network-based monitoring is promising in dealing with such data on nonlinear processes. This article proposes a deep discriminative representation learning method for efficient nonlinear plant-wide process monitoring. The key idea is to first decompose a large-scale process into multiple units according to a variable relationship, and then generate faulty validation data based on randomized fault construction. Then, deep discriminative representations are learned by optimizing a stacked autoencoder-based deep neural network (DNN). This article provides guidelines for designing an efficient monitoring algorithm for plant-wide nonlinear processes in the industrial big data environment.