A joint data-driven process monitoring method using knowledge propagation based on manifold clustering. (28th March 2021) - Zhang, Chuanfang; Peng, Kaixiang; Dong, Jie
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.