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

Zhang, J., Chen, M., Chen, H., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 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 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
URI	https://reading-clone.eprints-hosting.org/id/eprint/80781
Identification Number/DOI	10.1021/acs.iecr.8b05875
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	ACS Publications
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