Root causality analysis at early abnormal stage using principal component analysis and multivariate Granger causality
- Authors
- Pyun, Hahyung; Kim, Kyeongsu; Ha, Daegeun; Lee, Chul-Jin; Lee, Won Bo
- Issue Date
- Mar-2020
- Publisher
- Institution of Chemical Engineers
- Keywords
- Fault magnitude; Multivariate Granger causality (MVGC); Principal component analysis (PCA); Root causality
- Citation
- Process Safety and Environmental Protection, v.135, pp 113 - 125
- Pages
- 13
- Journal Title
- Process Safety and Environmental Protection
- Volume
- 135
- Start Page
- 113
- End Page
- 125
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/49919
- DOI
- 10.1016/j.psep.2019.12.010
- ISSN
- 0957-5820
1744-3598
- Abstract
- As fault detection technologies have been developed, process fault diagnosis at early abnormal stage has come to be considered a major problem. In this work, a method to analyze the root cause of faults is developed to provide proper information at the early abnormal stage. First, principal component analysis (PCA) is used for the early detection of the process fault. Then, the contributions, from which the normal portion is removed, are decomposed by singular value decomposition (SVD) method to select the hierarchical sensors. Finally, the multivariate Granger causality (MVGC) method is used to construct the sensor causalities using the hierarchical sensors. The developed methodology is verified using the liquefied natural gas fractionation process model, which embeds a sufficient number of highly correlated sensors. The results are compared with the conventional principal component analysis method and amplification of the residual contribution method to verify the advantages of the proposed method. © 2019 Institution of Chemical Engineers
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