International Journal of System Control and Information Processing
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International Journal of System Control and Information Processing (1 paper in press)
A joint data-driven process monitoring method using knowledge propagation based on manifold clustering by Chuanfang Zhang, Kaixiang Peng, Jie Dong 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 utilizes historical data containing knowledge information (labeled 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 unlabeled data. (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; Fault detection; Manifold clustering; Knowledge propagation.