Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description

Jian Huang; Okan K Ersoy; Xuefeng Yan

doi:10.1016/j.isatra.2018.10.016

Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description

ISA Trans. 2019 Feb:85:119-128. doi: 10.1016/j.isatra.2018.10.016. Epub 2018 Oct 24.

Authors

Jian Huang¹, Okan K Ersoy², Xuefeng Yan³

Affiliations

¹ Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
² School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. Electronic address: ersoy@purdue.edu.
³ Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China. Electronic address: xfyan@ecust.edu.cn.

PMID: 30389247
DOI: 10.1016/j.isatra.2018.10.016

Abstract

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.

Keywords: Fault detection; Multi-block algorithm; Slow feature analysis; Support vector data description.