Date of Award
2018-01-01
Degree Name
Master of Science
Department
Computational Science
Advisor(s)
Bill Tseng
Second Advisor
Jianguo Wu
Abstract
Most of the existing steady state detection approaches are designed for univariate signals. For multivariate signals, the univariate approach is often applied to each process variable and the system is claimed to be steady once all signals are steady, which is computationally inefficient and also not accurate. The article proposes an efficient online method for multivariate steady state detection. It estimates the covariance matrices using two different approaches, namely, the mean-squared-deviation and mean-squared-successive-difference. To avoid the usage of a moving window, the process means and the two covariance matrices are calculated recursively through exponentially weighted moving average. A likelihood ratio test is developed to compare the difference of the two covariance matrices and to detect the steady state. The intensive numerical studies and real case study show that the proposed method can accurately detect the steady state of a multivariate system.
Language
en
Provenance
Received from ProQuest
Copyright Date
2018-12
File Size
50 pages
File Format
application/pdf
Rights Holder
Honglun None Xu
Recommended Citation
Xu, Honglun None, "An Efficient Method For Online Identification Of Steady State For Multivariate System" (2018). Open Access Theses & Dissertations. 190.
https://scholarworks.utep.edu/open_etd/190