A Robust Fault Classification Method for Streaming Industrial Data Based on Wasserstein Generative Adversarial Network and Semi-Supervised Ladder Network

Abstract

With the development of modern information technology, the collection, storage and transmission of information in the process industry has been gaining popularity. However, the massive streaming industrial data obtained in real time has some non-ideal characteristics, such as lack of labels and missing values, which greatly increases the difficulty of process monitoring in process industry. Therefore, a robust semi-supervised fault classification method is proposed in this paper. First, Wasserstein generative adversarial network (WGAN) and enhanced minimal gated unit (EMGU) are integrated to complete the missing data imputation of the incomplete unlabeled streaming industrial data, and then semi-supervised ladder network (SLN) is trained with the imputed unlabeled data and complete labeled data for fault classification. A case study on the hot rolling process demonstrates that the proposed method shows outstanding modeling and classification performance in lack of labeled data and missing data, compared to the other state-of-art deep learning methods.