Title: A joint data-driven process monitoring method using knowledge propagation based on manifold clustering

Authors: Chuanfang Zhang; Kaixiang Peng; Jie Dong

Addresses: Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

Abstract: Compared with existing process monitoring approaches, a joint data-driven method using knowledge propagation based on manifold clustering is proposed for fault detection, which utilises historical data containing knowledge information (labelled data). The main contributions of this work are as follows: 1) two transformation matrices are derived based on manifold learning and clustering method; 2) different from conventional data-driven fault detection method, knowledge propagation based on manifold clustering is used to extract the features of unlabelled data; and 3) according to extracted features, the fault detection approach is proposed. The proposed method is applied to Tennessee Eastman (TE) process. The simulation results indicate that the proposed monitoring scheme can effectively monitor the working conditions of the process and identify fault types.

Keywords: joint data-driven; process monitoring; manifold clustering; knowledge propagation.

DOI: 10.1504/IJSCIP.2020.114252

International Journal of System Control and Information Processing, 2020 Vol.3 No.2, pp.77 - 92

Received: 28 Nov 2019
Accepted: 29 Nov 2019

Published online: 28 Mar 2021 *

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