Journal article
Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters
IEEE transactions on automation science and engineering, Vol.13(2), pp.688-700
04/2016
DOI: 10.1109/TASE.2014.2378150
Abstract
Steady-state detection is critical in process performance assessment, fault detection, and process automation and control. We proposed a robust online steady-state detection algorithm using multiple change-point model and particle filtering techniques. The steady-state detection problem is formulated as a multiple change-point problem using a segmented linear model. A particle filtering algorithm with stratified importance sampling and partial Gibbs move is developed to estimate this model. A generic timeliness improvement strategy is proposed to reduce the detection delay. Extensive numerical analysis shows that the proposed method is more accurate and robust than the other existing methods.
Details
- Title: Subtitle
- Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters
- Creators
- Jianguo Wu - University of Wisconsin–MadisonYong Chen - University of IowaShiyu Zhou - University of Wisconsin–MadisonXiaochun Li - University of California, Los Angeles
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on automation science and engineering, Vol.13(2), pp.688-700
- Publisher
- IEEE
- DOI
- 10.1109/TASE.2014.2378150
- ISSN
- 1545-5955
- eISSN
- 1558-3783
- Grant note
- 1335129 / National Science Foundation; National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 04/2016
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984187073502771
Metrics
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