Authors: Jian Yang; Hongbo Shi
Addresses: Key Laboratory of Advanced Control and Optimization for Chemical Process of the Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China ' Key Laboratory of Advanced Control and Optimization for Chemical Process of the Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China
Abstract: In this paper, a novel dimensionality reduction method, time-space coordinated-locality preserving projections (TSC-LPP) is proposed based on locality preserving projection (LPP). In practical process, except the data correlation in spatial scale, there exists data correlation in time scale as well for the short sampling interval. To considering the correlation of sampling points in time and spatial scale simultaneously, TSC-LPP constructs the adjacency graph by selecting adjacent points in time sequence and Euclidean distance, respectively. Furthermore, the importance of the time-sequential neighbours is measured by the computed weight based on time distance. A dual objective function with a weight index coordinating the relationship between time and space is constructed to compute the transformation matrix. Hotelling's T2 and squared prediction error (SPE) are established for process monitoring. A numerical case and the Tennessee-Eastman process (TEP) are employed for the experimental verification.
Keywords: dimensionality reduction; locality preserving projection; LPP; time sequence; Euclidean distance; process monitoring.
International Journal of System Control and Information Processing, 2017 Vol.2 No.2, pp.99 - 112
Received: 13 Oct 2016
Accepted: 06 Mar 2017
Published online: 08 Feb 2018 *