A joint data-driven process monitoring method using knowledge propagation based on manifold clustering Online publication date: Thu, 15-Apr-2021
by Chuanfang Zhang; Kaixiang Peng; Jie Dong
International Journal of System Control and Information Processing (IJSCIP), Vol. 3, No. 2, 2020
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of System Control and Information Processing (IJSCIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook