Journal
ISA TRANSACTIONS
Volume 85, Issue -, Pages 119-128Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2018.10.016
Keywords
Multi-block algorithm; Slow feature analysis; Support vector data description; Fault detection
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Funding
- National Natural Science Foundation of China [21878081]
- Fundamental Research Funds for the Central Universities [222201717006]
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This study describes a dynamic large-scale process fault detection algorithm based on multi-block slow feature analysis by taking advantages of both multi-block algorithms in highlighting the local information and slow feature analysis in extracting the different dynamics of process data. A mutual information-based relevance matrix is first calculated to measure the correlation between any two variables, and then K-means clustering is used to cluster the original variables into several blocks by gathering the variables with similar relevance vectors into the same block. Slow feature analysis is applied in each block. A support vector data description is utilized to give a final decision. The proposed algorithm is tested with a well-known Tennessee Eastman (TE) process. The fault detection results show the efficiency and the superiority of the proposed method as compared to other related methods. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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