Teacher–Student Uncertainty Autoencoder for the Process-Relevant and Quality-Relevant Fault Detection in the Industrial Process
Dan Yang, Xin Peng, Yusheng Lu, Haojie Huang, Weimin Zhong
Abstract
Fault detection plays an important role in process monitoring, while current fault detection methods only concentrate on process-relevant or quality-relevant faults. Therefore, in this article, a fault detection method based on the improved teacher–student network is proposed, in which both the process-relevant and quality-relevant faults are monitored. Concretely, the student network extracts representation features and the teacher network detects faults. As the features difference between the teacher and student networks can cause performance degradation when the features of teacher network are replaced by the one from student network, representation evaluation block (REB) is proposed to evaluate and reduce the features difference. As a concrete method of REB, uncertainty modeling is proposed to quantify the features difference and alleviate the aleatoric uncertainty, modeling features difference as a central isotropic Gaussian distribution. Then, asynchronous iterative is designed to implement teacher network and student network joint training. Accordingly, REB based on uncertainty modeling is applied in the teacher–student network named as teacher–student uncertainty autoencoder (TSUAE). Finally, a fault detection framework based on TSUAE is proposed, the effectiveness of which is verified in a wastewater treatment process.