Consistency Regularization Auto-Encoder Network for Semi-Supervised Process Fault Diagnosis

Abstract

Industrial processes are developing towards intelligence and complexity, which brings challenges to intelligent process monitoring. An effective fault diagnosis model plays a vital role in ensuring process safety. However, labeling process samples is time-consuming and costly, which make it hard to obtain enough labeled samples to train an effective diagnostic model. This motivates the development of semi-supervised learning which basic idea is to use unlabeled data to help limited labeled data for model training. In this paper, a consistency regularization auto-encoder (CRAE) framework based on encoder-decoder network is proposed to overcome the problem caused by limited labeled samples. The proposed CRAE captured temporal and spatial correlations from both labeled and unlabeled samples to realize fault diagnosis. Firstly, single process sample is processed as sample matrix using proposed data augmented strategy which can help the proposed method to extract more representative features. Secondly, local encoder and global encoder are proposed to extract local and global temporal and spatial features from sample matrices. Next, the local and global features are fused as the input of decoder network, which improve the ability of reconstruction. Finally, the consistency regularization method is introduced into the encoder-decoder framework to push the decision boundary to the low-density area, which make the distinction among different categories more obvious to help the model better achieve the classification task. Experiments on the Tennessee Eastman process show that the proposed method is effective for process fault diagnosis when labeled samples are limited compared to the other semi-supervised algorithms.