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.
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ASME 2018 13th International Manufacturing Science and Engineering Conference
June 18–22, 2018
College Station, Texas, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5138-8
PROCEEDINGS PAPER
An Efficient Method for Online Identification of Steady State for Multivariate System Available to Purchase
Honglun Xu,
Honglun Xu
University of Texas at El Paso, El Paso, TX
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Tzu-Liang (Bill) Tseng
Tzu-Liang (Bill) Tseng
University of Texas at El Paso, El Paso, TX
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Honglun Xu
University of Texas at El Paso, El Paso, TX
Jianguo Wu
Peking University, Beijing, China
Tzu-Liang (Bill) Tseng
University of Texas at El Paso, El Paso, TX
Paper No:
MSEC2018-6565, V004T03A005; 9 pages
Published Online:
September 24, 2018
Citation
Xu, H, Wu, J, & Tseng, T(. "An Efficient Method for Online Identification of Steady State for Multivariate System." Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference. Volume 4: Processes. College Station, Texas, USA. June 18–22, 2018. V004T03A005. ASME. https://doi.org/10.1115/MSEC2018-6565
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