Accessibility navigation


Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings

Zhang, J., Chen, M. and Hong, X. (2021) Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings. Neurocomputing, 458. pp. 319-326. ISSN 0925-2312

[img] Text - Accepted Version
· Restricted to Repository staff only until 12 June 2022.
· Available under License Creative Commons Attribution Non-commercial No Derivatives.

958kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.neucom.2021.06.039

Abstract/Summary

Motivated by mixture of probabilistic principal component analysis (PCA), which is time-consumingdue to expectation maximization, this paper investigates a novel mixture of probabilistic PCA withclusterings for process monitoring. The significant features are extracted by singular vector decom-position (SVD) or kernel PCA, and k-means is subsequently utilized as a clustering algorithm. Then, parameters of local PCA models are determined under each clustering model. Compared with PCA clustering, SVD based clustering only utilizes the nature basis for the components of the data instead of principal components of the data. Three clustering approaches are adopted and the effectiveness of the proposed approach is demonstrated by a practical coal pulverizing system.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:99132
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation