Process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman Process. Issue 2 (2022)

• Record Type: Journal Article
• Authors: Uchida, Yoshiaki; Fujiwara, Koichi; Saito, Tatsuki; Osaka, Taketsugu
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 2(2022)
• Page Start: 384
• Page End: 389
• Publication Date: 2022
• Subjects: Fault detection -- diagnosis -- Multivariate statistical process control -- Linear non-Gaussian acyclic model -- Causal inference -- Contribution plot; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.04.224
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper proposes a new fault diagnosis method that combines Multivariate statistical process control (MSPC) and a linear non-gaussian acyclic model (LiNGAM), referred to as MSPC-LiNGAM. MSPC is a widely adopted process monitoring method based on principal component analysis (PCA). In MSPC, T 2 and Q statistics are used as monitoring indexes for fault detection. Contribution plots based on T 2 and Q statistics have been proposed for fault diagnosis. However, contribution plots do not always appropriately diagnose causes of faults. In this study, a new fault diagnosis method based on MSPC and a Linear Non-Gaussian Acyclic Model (LiNGAM) is proposed. In the proposed method, referred to as MSPC-LiNGAM, the causality among the T 2 or Q statistic in addition to process variables is calculated by LiNGAM without prior knowledge of processes, and process variables that have the strength of causality to the T 2 or Q statistic are identified as candidates of the causes of the fault. The proposed MSPC-LiNGAM was applied to a simulation data of the Tennessee Eastman (TE) process. The result showed that the proposed method appropriately diagnosed faults even when the conventional contribution plots did not correctly identify causes of faults.