Research on TE process fault diagnosis method based on DBN and dropout. (14th April 2020)

• Record Type: Journal Article
• Authors: Wei, Yuqin; Weng, Zhengxin
• Is Part Of: Canadian journal of chemical engineering. Volume 98:Number 6(2020)
• Page Start: 1293
• Page End: 1306
• Publication Date: 2020-04-14
• Subjects: deep belief network -- dropout -- fault diagnosis -- Tennessee Eastman process; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.23750
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: In recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN‐dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the real‐time monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach.