A process monitoring and fault isolation framework based on variational autoencoders and branch and bound method. Issue 2 (January 2022)

• Record Type: Journal Article
• Authors: Tang, Peng; Peng, Kaixiang; Jiao, Ruihua
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 2(2022)
• Page Start: 1667
• Page End: 1691
• Publication Date: 2022-01
• Subjects: Process monitoring -- Fault isolation -- Variational autoencoder -- Missing value estimation -- Branch and bound; Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2021.11.016
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: Nonlinear characteristic widely exists in industrial processes. Many approaches based on kernel methods and machine learning have been developed for nonlinear process monitoring. However, the fault isolation for nonlinear processes has rarely been studied in previous works. In this paper, a process monitoring and fault isolation framework is proposed for nonlinear processes using variational autoencoder (VAE) model. First, based on the probability graph model of VAE, a uniform monitoring index can be calculated by the probability density of observation variables. Then, the fault variables are estimated with normal variables by a missing value estimation method. The optimal fault variable set can be searched by branch and bound (BAB) algorithm. The proposed method can resolve the "smearing effects" problem existing in traditional fault isolation methods. Finally, a numerical case and a hot strip mill process case are used to verified the proposed method.