High-fidelity positive-unlabeled deep learning for semi-supervised fault detection of chemical processes

Abstract

With the rapid development of the modern chemical process industry, process monitoring techniques have been investigated to enhance the loss prevention capability. Fault detection which attempts to detect whether an abnormal condition has happened is an essential step of process monitoring. Supervised learning based fault detection methods usually are not feasible in practical industry situations, because their most important prerequisite that adequate data labels are accessible is extremely hard to meet. In real industrial situations, a common scenario is that a few normal state data are labeled while no fault state data are labeled, and this is a natural fit for the positive-unlabeled (PU) learning. In this work, a three-step high-fidelity PU (THPU) approach based on deep learning is proposed for semi-supervised fault detection of chemical processes. A self-training nearest neighbors (STNN) algorithm is designed in Step 1 to conservatively generate a small reliable positive dataset by extracting data from the unlabeled training dataset. A positive and negative data recognition (PNDR) algorithm is designed in Step 2 to augment the reliable positive dataset and generate a reliable negative dataset based on the remaining unlabeled dataset. A convolutional neural network is trained in Step 3 based on the above reliable datasets as the binary classifier for online fault detection. The benchmark simulated Tennessee Eastman process dataset and a real industrial hydro-cracking process dataset are utilized to illustrate the effectiveness and robustness of the proposed THPU approach. The experimental results demonstrate the superiority of the proposed approach compared to the other competing PU learning approaches and supervised fault detection models.