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Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

Zhang, J., Chen, M., Chen, H., Hong, X. and Zhou, D. (2019) Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation. Industrial & Engineering Chemistry Research, 58 (14). pp. 5579-5587. ISSN 1520-5045

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To link to this item DOI: 10.1021/acs.iecr.8b05875

Abstract/Summary

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection TOLPP2 and SPEOLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:80781
Publisher:ACS Publications

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